Gustavo Carneiro

Research Interests

Biomedical Engineering Computer Vision Knowledge Representation and Machine Learning

Prof Gustavo Carneiro

School of Mathematical Sciences

College of Science

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

I have focused my research on the development and application of computer vision and machine learning techniques, with particular emphasis on medical image analysis problems.  For more details on the current (and past) research problems, please check this page. 

Date Position Institution name
2015 - ongoing Associate Professor University of Adelaide
2014 - 2015 Humboldt Experienced Researcher Technical University of Munich
2011 - 2014 Senior Lecturer University of Adelaide
2011 - 2011 Marie Curie International Incoming Fellow University of Lisbon
2008 - 2010 Visiting Assistant Professor University of Lisbon
2006 - 2008 Senior Research Scientist Siemens Corporate Research
2004 - 2005 Postdoctoral Fellow University of British Columbia
2004 - 2004 Postdoctoral Fellow University of California, San Diego

Language Competency
English Can read, write, speak, understand spoken and peer review
French Can read
Portuguese Can read, write, speak, understand spoken and peer review
Spanish; Castilian Can read and understand spoken

Date Institution name Country Title
1999 - 2004 University of Toronto Canada PhD
1997 - 1999 Instituto Militar de Engenharia Brazil MSc
1992 - 1996 Universidade Federal do Rio de Janeiro Brazil Bachelor's degree

Year Citation
2026 Zhang, Z., Nguyen, C., Wells, K., Do, T. T., & Carneiro, G. (2026). Learning to complement with multiple humans. Pattern Recognition, 172, 12 pages.
DOI
2025 Wang, Y., Song, K., Liu, Y., Li, T., Yan, Y., & Carneiro, G. (2025). Bimodal defect segmentation with Geometric Prior-supported Anti-imbalance Learning for pavement defect evaluation and repair. Automation in Construction, 180, 106497.
DOI
2025 Khakpour, A., Florescu, L., Tilley, R., Jiang, H., Iyer, K. S., & Carneiro, G. (2025). AI-powered prediction of nanoparticle pharmacokinetics: A multi-view learning approach. Materials Today Communications, 49, 113742.
DOI Scopus3
2025 Zhang, Y., Wang, H., Butler, D., Smart, B., Xie, Y., To, M. -S., . . . Carneiro, G. (2025). Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis. Computerized Medical Imaging and Graphics, 124, 102575.
DOI
2025 Hoang, D. A., Nguyen, C., Belagiannis, V., Do, T. T., & Carneiro, G. (2025). Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning. Transactions on Machine Learning Research, 2025-March.
2025 Wang, C., Chen, Y., Liu, F., Liu, Y., McCarthy, D. J., Frazer, H., & Carneiro, G. (2025). Mixture of Gaussian-Distributed Prototypes With Generative Modelling for Interpretable and Trustworthy Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(8), 6974-6989.
DOI Scopus1 Europe PMC1
2025 Wang, Y., Song, K., Liu, Y., Ma, S., Yan, Y., & Carneiro, G. (2025). Leveraging labelled data knowledge: A cooperative rectification learning network for semi-supervised 3D medical image segmentation. Medical Image Analysis, 101, 15 pages.
DOI Scopus5 WoS6
2025 Wang, C., Liu, F., Chen, Y., Frazer, H., & Carneiro, G. (2025). Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation. IEEE Transactions on Medical Imaging, 44(6), 2568-2580.
DOI Scopus8 WoS6 Europe PMC2
2025 Assié, G., Carneiro, G., Davies, E., Šojat, A. S., & Mertens, J. (2025). EndoCompass project: artificial intelligence in endocrinology. European Journal of Endocrinology, 193(Supplement_2), ii170-ii176.
DOI
2025 Wang, C., Liu, F., Chen, Y., Kwok, C. F., Elliott, M., Pena-Solorzano, C., . . . Carneiro, G. (2025). Progressive Mining and Dynamic Distillation of Hierarchical Prototypes for Disease Classification and Localisation. IEEE Journal of Biomedical and Health Informatics, 29(8), 5687-5699.
DOI Scopus2 WoS2
2025 Wang, H., Lu, N., Wang, Z., Yan, Y., Carneiro, G., & Wang, Z. (2025). Self-Correcting Clustering. IEEE Transactions on Knowledge and Data Engineering, 37(3), 1439-1454.
DOI Scopus1
2025 Cordeiro, F. R., & Carneiro, G. (2025). ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels. Pattern Recognition, 159, 10 pages.
DOI Scopus5 WoS5
2024 Frazer, H. M. L., Peña-Solorzano, C. A., Kwok, C. F., Elliott, M. S., Chen, Y., Wang, C., . . . McCarthy, D. J. (2024). Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer. Nature Communications, 15(1), 7525-1-7525-12.
DOI Scopus22 WoS20 Europe PMC15
2024 Wang, H., Butler, D., Zhang, Y., Avery, J., Knox, S., Ma, C., . . . Carneiro, G. (2024). Human-AI collaborative multi-modal multi-rater learning for endometriosis diagnosis.. Physics in medicine and biology, 70(1), 13 pages.
DOI Scopus3 WoS3 Europe PMC2
2024 Hermoza, R., Nascimento, J. C., & Carneiro, G. (2024). Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images. Computerized Medical Imaging and Graphics, 115, 102395-1-102395-10.
DOI Scopus5 WoS3 Europe PMC1
2024 Tan, J. L., Pitawela, D., Chinnaratha, M. A., Beany, A., Aguila, E. J., Chen, H. T., . . . Singh, R. (2024). Exploring vision transformers for classifying early Barrett's dysplasia in endoscopic images: A pilot study on white-light and narrow-band imaging. JGH Open, 8(9), 7 pages.
DOI Scopus2
2024 Liu, Y., Tian, Y., Wang, C., Chen, Y., Liu, F., Belagiannis, V., & Carneiro, G. (2024). Translation Consistent Semi-supervised Segmentation for 3D Medical Images. IEEE Transactions on Medical Imaging, 44(2), 952-968.
DOI Scopus9 WoS6 Europe PMC2
2024 Vente, C. D., Vermeer, K. A., Jaccard, N., Wang, H., Sun, H., Khader, F., . . . Sánchez, C. I. (2024). AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.. IEEE Trans. Medical Imaging, 43, 542-557.
2024 Tapper, W., Carneiro, G., Mikropoulos, C., Thomas, S. A., Evans, P. M., & Boussios, S. (2024). The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer. Journal of Personalized Medicine, 14(3), 19 pages.
DOI Scopus36 WoS30 Europe PMC24
2024 Yap, M. H., Cassidy, B., Byra, M., Liao, T. Y., Yi, H., Galdran, A., . . . Kendrick, C. (2024). Diabetic foot ulcers segmentation challenge report: Benchmark and analysis. Medical Image Analysis, 94, 14 pages.
DOI Scopus32 WoS28 Europe PMC6
2024 Chen, Y., Liu, Y., Wang, C., Elliott, M., Kwok, C. F., Peña-Solorzano, C., . . . Carneiro, G. (2024). BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations. Medical Image Analysis, 96, 103192-1-103192-13.
DOI Scopus12 WoS10 Europe PMC6
2024 Tian, Y., Zorron Cheng Tao Pu, L., Liu, Y., Maicas, G., Verjans, J. W., Burt, A. D., . . . Carneiro, G. (2024). Detecting, localizing and classifying polyps from colonoscopy videos using deep learning. Unknown Journal, 425-450.
DOI
2024 Avery, J. C., Knox, S., Deslandes, A., Leonardi, M., Lo, G., Wang, H., . . . Imagendo Study Group. (2024). Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography. Fertility and Sterility, 121(2), 189-211.
DOI Scopus19 WoS17 Europe PMC10
2024 Avery, J. C., Deslandes, A., Freger, S. M., Leonardi, M., Lo, G., Carneiro, G., . . . Imagendo Study Group. (2024). Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence. Fertility and Sterility, 121(2), 164-188.
DOI Scopus37 WoS32 Europe PMC23
2024 Wang, C., Chen, Y., Liu, F., Elliott, M., Kwok, C. F., Pena-Solorzano, C., . . . Carneiro, G. (2024). An Interpretable and Accurate Deep-learning Diagnosis Framework Modelled with Fully and Semi-supervised Reciprocal Learning. IEEE Transactions on Medical Imaging, 43(1), 392-404.
DOI Scopus32 WoS23 Europe PMC11
2024 Bedrikovetski, S., Zhang, J., Seow, W., Traeger, L., Moore, J. W., Verjans, J., . . . Sammour, T. (2024). Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.. J Med Imaging Radiat Oncol, 68(1), 33-40.
DOI Scopus4 WoS4 Europe PMC4
2023 Nguyen, C., Do, T. -T., & Carneiro, G. (2023). Towards the Identifiability in Noisy Label Learning: A Multinomial
Mixture Approach.
2023 Vente, C. D., Vermeer, K. A., Jaccard, N., Wang, H., Sun, H., Khader, F., . . . Sánchez, C. I. (2023). AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge.. CoRR, abs/2302.01738.
2023 Hollis-Sando, L., Pugh, C., Franke, K., Zerner, T., Tan, Y., Carneiro, G., . . . Bacchi, S. (2023). Deep learning in the marking of medical student short answer question examinations: Student perceptions and pilot accuracy assessment. FOCUS ON HEALTH PROFESSIONAL EDUCATION-A MULTIDISCIPLINARY JOURNAL, 24(1), 38-48.
DOI WoS4
2023 Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W., . . . Carneiro, G. (2023). Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis, 90, 102930-1-102930-11.
DOI Scopus31 WoS18 Europe PMC7
2023 Wang, C., Cui, Z., Yang, J., Han, M., Carneiro, G., & Shen, D. (2023). BowelNet: Joint Semantic-Geometric Ensemble Learning for Bowel Segmentation From Both Partially and Fully Labeled CT Images. IEEE Transactions on Medical Imaging, 42(4), 1225-1236.
DOI Scopus16 WoS13 Europe PMC7
2023 Sachdeva, R., Cordeiro, F. R., Belagiannis, V., Reid, I., & Carneiro, G. (2023). ScanMix : Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning. Pattern Recognition, 134, 109121-1-109121-10.
DOI Scopus34 WoS28
2023 Frazer, H. M. L., Tang, J. S. N., Elliott, M. S., Kunicki, K. M., Hill, B., Karthik, R., . . . McCarthy, D. J. (2023). ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets. Radiology: Artificial Intelligence, 5(2), 1-7.
DOI Scopus27 WoS23 Europe PMC20
2023 Galdran, A., Verjans, J. W., Carneiro, G., & González Ballester, M. A. (2023). Multi-Head Multi-Loss Model Calibration. Lecture Notes in Computer Science, 14222, 108-117.
DOI Scopus9 WoS7
2023 Cordeiro, F. R., Sachdeva, R., Belagiannis, V., Reid, I., & Carneiro, G. (2023). LongReMix: Robust learning with high confidence samples in a noisy label environment. Pattern Recognition, 133, 109013-1-109013-14.
DOI Scopus87 WoS74
2023 De Vente, C., Vermeer, K. A., Jaccard, N., Wang, H., Sun, H., Khader, F., . . . Sanchez, C. I. (2023). AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge. IEEE Transactions on Medical Imaging, PP(1), 1.
DOI Scopus42 WoS18 Europe PMC17
2023 Nguyen, C., Do, T. -T., & Carneiro, G. (2023). PAC-Bayes meta-learning with implicit task-specific posteriors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 841-851.
DOI Scopus7 WoS5 Europe PMC1
2023 Avery, J., Zhang, Y., Wang, H., O’Hara, R., Condous, G., Leonardi, M., . . . Hull, M. -L. (2023). #122 : Extrapolating Endometriosis Diagnosis Using Imaging and Machine Learning: The Imagendo Project. Fertility & Reproduction, 05(04), 527.
DOI
2023 Nguyen, C., Do, T. T., & Carneiro, G. (2023). Task Weighting in Meta-learning with Trajectory Optimisation. Transactions on Machine Learning Research, 2023.
Scopus1
2022 Frazer, H. M. L., Peña-Solorzano, C., Kwok, C. F., Elliott, M., Chen, Y., Wang, C., . . . McCarthy, D. (2022). Comparison of AI-integrated pathways with human-AI interaction for population mammographic screening.
DOI
2022 Chen, H. -T., Zhang, Y., Carneiro, G., Shin, S. H., & Singh, R. (2022). Toward a Human-Centered AI-assisted Colonoscopy System.
2022 Tan, J. L., Chinnaratha, M. A., Woodman, R., Martin, R., Chen, H. -T., Carneiro, G., & Singh, R. (2022). Diagnostic Accuracy of Artificial Intelligence (AI) to Detect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis. Frontiers in Medicine, 9, 1-11.
DOI Scopus20 WoS19 Europe PMC17
2022 Oakden-Rayner, L., Gale, W., Bonham, T. A., Lungren, M. P., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2022). Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study.. Lancet Digit Health, 4(5), e351-e358.
DOI Scopus68 WoS62 Europe PMC49
2022 Galdran, A., Hewitt, K. J., Laleh, N. G., Kather, J. N., Carneiro, G., & Ballester, M. Á. G. (2022). Test Time Transform Prediction for Open Set Histopathological Image Recognition.. CoRR, abs/2206.10033.
2022 Moura, T. C. M. S., Arruda, L. C. P. M., Silva, R. J. A. F., Silva, R. F., Oliveira, A., Tobal, L. M., . . . Guerra, M. M. P. (2022). Diluent Containing Dimethylformamide Added With Sucrose Improves <i>In Vitro</i> Quality After Freezing/Thawing Stallion Sperm. JOURNAL OF EQUINE VETERINARY SCIENCE, 109, 5 pages.
DOI WoS5
2021 Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W., . . . Carneiro, G. (2021). Multi-centred Strong Augmentation via Contrastive Learning for Unsupervised Lesion Detection and Segmentation.. CoRR, abs/2109.01303.
2021 Galdran, A., Carneiro, G., & Ballester, M. Á. G. (2021). Balanced-MixUp for Highly Imbalanced Medical Image Classification.. CoRR, abs/2109.09850.
2021 Galdran, A., Carneiro, G., & Ballester, M. Á. G. (2021). Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation.. CoRR, abs/2110.01939.
2021 Galdran, A., Carneiro, G., & Ballester, M. Á. G. (2021). Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification.. CoRR, abs/2111.06894.
2021 Santiago, C., Barata, C., Sasdelli, M., Carneiro, G., & Nascimento, J. C. (2021). LOW: Training deep neural networks by learning optimal sample weights. Pattern Recognition, 110, 1-12.
DOI Scopus43 WoS36
2021 Ang, T. L., & Carneiro, G. (2021). Artificial intelligence in gastrointestinal endoscopy. Journal of Gastroenterology and Hepatology (Australia), 36(1), 5-6.
DOI Scopus12 WoS11 Europe PMC10
2021 David, R., Menezes, R. -J. D., De Klerk, J., Castleden, I. R., Hooper, C. M., Carneiro, G., & Gilliham, M. (2021). Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network. Scientific Reports, 11(1), 1-11.
DOI Scopus4 WoS2 Europe PMC4
2021 Bedrikovetski, S., Dudi-Venkata, N. N., Maicas Suso, G., Kroon, H. M., Seow, W., Carneiro, G., . . . Sammour, T. (2021). Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: a systematic review and meta-analysis. Artificial Intelligence in Medicine, 113, 1-11.
DOI Scopus33 WoS28 Europe PMC25
2021 Nguyen, L. V., Nguyen, C. C., Carneiro, G., Ebendorff-Heidepriem, H., & Warren-Smith, S. C. (2021). Sensing in the presence of strong noise by deep learning of dynamic multimode fiber interference. Photonics Research, 9(4), 109-118.
DOI Scopus75 WoS65
2021 Banach, A., Strydom, M., Jaiprakash, A., Carneiro, G., Eriksson, A., Crawford, R., & McFadyen, A. (2021). Visual Localisation for Knee Arthroscopy. International Journal of Computer Assisted Radiology and Surgery, 16(12), 2137-2145.
DOI Scopus5 WoS5 Europe PMC3
2021 Condon, J. J. J., Oakden-Rayner, L., Hall, K. A., Reintals, M., Holmes, A., Carneiro, G., & Palmer, L. J. (2021). Replication of an open-access deep learning system for screening mammography: Reduced performance mitigated by retraining on local data.
DOI
2021 Bedrikovetski, S., Dudi-Venkata, N. N., Kroon, H. M., Seow, W., Vather, R., Carneiro, G., . . . Sammour, T. (2021). Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer, 21(1), 1058-1-1058-10.
DOI Scopus120 WoS111 Europe PMC98
2021 van der Burgt, J. M. A., Camps, S. M., Antico, M., Carneiro, G., & Fontanarosa, D. (2021). Arthroscope localization in 3D ultrasound volumes using weakly supervised deep learning. Applied Sciences, 11(15), 1-13.
DOI
2021 Maicas Suso, G., Leonardi, M., Avery, J., Panuccio, C., Carneiro, G., Hull, M. L., & Condous, G. (2021). Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign. Reproduction and Fertility, 2(4), 236-243.
DOI Scopus30 WoS26 Europe PMC14
2020 Antico, M., Sasazawa, F., Takeda, Y., Jaiprakash, A. T., Wille, M. L., Pandey, A. K., . . . Fontanarosa, D. (2020). Bayesian CNN for Segmentation Uncertainty Inference on 4D Ultrasound Images of the Femoral Cartilage for Guidance in Robotic Knee Arthroscopy. IEEE Access, 8, 223961-223975.
DOI Scopus21 WoS19
2020 Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy.. CoRR, abs/2006.14811.
2020 Ranasinghe, I., Hossain, S., Ali, A., Horton, D., Adams, R. J., Aliprandi-Costa, B., . . . Woodman, R. J. (2020). SAFety, Effectiveness of care and Resource use among Australian Hospitals (SAFER Hospitals): a protocol for a population-wide cohort study of outcomes of hospital care. BMJ, 10(8), e035446-1-e035446-9.
DOI Scopus2 WoS2 Europe PMC2
2020 Carneiro, G., Zorron Cheng Tao Pu, L., Singh, R., & Burt, A. (2020). Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Medical Image Analysis, 62, 1-13.
DOI Scopus59 WoS53 Europe PMC23
2020 Jonmohamadi, Y., Takeda, Y., Liu, F., Sasazawa, F., Maicas, G., Crawford, R., . . . Carneiro, G. (2020). Automatic segmentation of multiple structures in knee arthroscopy using deep learning. IEEE Access, 8, 51853-51861.
DOI Scopus30 WoS19
2020 Angelova, A., Carneiro, G., Sünderhauf, N., & Leitner, J. (2020). Special Issue on Deep Learning for Robotic Vision. International Journal of Computer Vision, 128(5), 2 pages.
DOI Scopus2 WoS1
2020 Nascimento, J. C., & Carneiro, G. (2020). One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(12), 3054-3070.
DOI Scopus13 WoS11 Europe PMC4
2020 Banach, A., Strydom, M., Jaiprakash, A., Carneiro, G., Brown, C., Crawford, R., & McFadyen, A. (2020). Saliency improvement in feature-poor surgical environments using Local Laplacian of Specified Histograms. IEEE Access, 8, 213378-213388.
DOI Scopus2 WoS2
2020 Carneiro, G., Tavares, J. M. R. S., Bradley, A. P., Papa, J. P., Belagiannis, V., Nascimento, J. C., & Lu, Z. (2020). Special issue: 4<sup>th</sup> MICCAI workshop on deep learning in medical image analysis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 8(5), 501.
DOI Scopus1 WoS1
2020 Cheng Tao Pu, L. Z., Maicas, G., Tian, Y., Yamamura, T., Nakamura, M., Suzuki, H., . . . Singh, R. (2020). Computer-aided diagnosis for characterisation of colorectal lesions: a comprehensive software including serrated lesions. Gastrointestinal Endoscopy, 92(4), 891-899.
DOI Scopus43 WoS39 Europe PMC23
2020 Dunnhofer, M., Antico, M., Sasazawa, F., Takeda, Y., Camps, S., Martinel, N., . . . Fontanarosa, D. (2020). Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Medical Image Analysis, 60, 101631-1-101631-17.
DOI Scopus72 WoS64 Europe PMC23
2020 Antico, M., Fontanarosa, D., Carneiro, G., Vukovic, D., Camps, S. M., Sasazawa, F., . . . Crawford, R. (2020). Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2543-2552.
DOI Scopus10 WoS8 Europe PMC3
2020 David, R., Menezes, R. -J., De Klerk, J., Castleden, I., Hooper, C., Carneiro, G., & Gilliham, M. (2020). Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.
DOI Europe PMC1
2020 Le, H. -S., Akmeliawati, R., & Carneiro, G. (2020). Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract).. CoRR, abs/2012.13973.
2020 Carneiro, G. F., Ferreira, M. P., & de Sá Volotão, C. F. (2020). Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations. Revista Brasileira de Cartografia, 72(1), 110-124.
DOI Scopus1
2020 Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2020). Approximate Fisher information matrix to characterise the training of deep neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(1), 15-26.
DOI Scopus21 WoS16 Europe PMC1
2020 Antico, M., Sasazawa, F., Dunnhofer, M., Camps, S. M., Jaiprakash, A. T., Pandey, A. K., . . . Fontanarosa, D. (2020). Deep learning-based femoral cartilage automatic segmentation in ultrasound imaging for guidance in robotic knee arthroscopy. Ultrasound in Medicine and Biology, 46(2), 422-435.
DOI Scopus36 WoS34 Europe PMC21
2020 Camps, S. M., Houben, T., Carneiro, G., Edwards, C., Antico, M., Dunnhofer, M., . . . Fontanarosa, D. (2020). Automatic quality assessment of transperineal ultrasound images of the male pelvic region, using deep learning. Ultrasound in Medicine and Biology, 46(2), 445-454.
DOI Scopus13 WoS11 Europe PMC9
2019 Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised zero-shot learning with a classifier ensemble over multi-modal embedding spaces. arXiv, abs/1908.02013, 1-9.
2019 Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. Medical Image Analysis, 58, 101562-1-101562-14.
DOI Scopus30 WoS24 Europe PMC14
2019 Carneiro, G., Manuel, J., Tavares, R. S., Bradley, A. P., Papa, J. P., Nascimento, J. C., . . . Belagiannis, V. (2019). Editorial. Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 7(3), 241.
DOI Scopus1 WoS1
2019 Liu, Y., Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2019). Photoshopping Colonoscopy Video Frames.. CoRR, abs/1910.10345.
2019 Glaser, S., Maicas, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2019). Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT.. CoRR, abs/1910.10371.
2019 Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space.. CoRR, abs/1908.04930.
2019 Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443.
DOI WoS3
2018 Snaauw, G., Gong, D., Maicas, G., Hengel, A. V. D., Niessen, W. J., Verjans, J., & Carneiro, G. (2018). End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging.. CoRR, abs/1810.10117.
2018 Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2018). Producing radiologist-quality reports for interpretable artificial intelligence.. CoRR, abs/1806.00340.
2018 Carneiro, G., Tavares, J. M. R. S., Bradley, A. P., Papa, J. P., Nascimento, J. C., Cardoso, J. S., . . . Belagiannis, V. (2018). 1st MICCAI workshop on deep learning in medical image analysis. Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 6(3), 241-242.
DOI
2017 Dhungel, N., Carneiro, G., & Bradley, A. (2017). A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical Image Analysis, 37, 114-128.
DOI Scopus309 WoS243 Europe PMC108
2017 Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2017). Detecting hip fractures with radiologist-level performance using deep neural networks.. CoRR, abs/1711.06504.
2017 Carneiro, G., Nascimento, J., & Bradley, A. (2017). Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Transactions on Medical Imaging, 36(11), 2355-2365.
DOI Scopus179 WoS145 Europe PMC52
2017 Nascimento, J., & Carneiro, G. (2017). Deep learning on sparse manifolds for faster object segmentation. IEEE Transactions on Image Processing, 26(10), 4978-4990.
DOI Scopus14 WoS13 Europe PMC4
2017 Ngo, T. A., Lu, Z., & Carneiro, G. (2017). Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Medical Image Analysis, 35, 159-171.
DOI Scopus301 WoS258 Europe PMC127
2017 Lu, Z., Carneiro, G., Bradley, A., Ushizima, D., Nosrati, M., Bianchi, A., . . . Hamarneh, G. (2017). Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE Journal of Biomedical and Health Informatics, 21(2), 441-450.
DOI Scopus128 WoS107 Europe PMC42
2017 Ribeiro, D., Nascimento, J., Bernardino, A., & Carneiro, G. (2017). Improving the performance of pedestrian detectors using convolutional learning. Pattern Recognition, 61, 641-649.
DOI Scopus34 WoS29
2017 Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J., Bradley, A., & Palmer, L. (2017). Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports, 7(1), 13 pages.
DOI Scopus127 WoS105 Europe PMC86
2017 Liao, Z., & Carneiro, G. (2017). A deep convolutional neural network module that promotes competition of multiple-size filters. Pattern Recognition, 71, 94-105.
DOI Scopus25 WoS24
2017 Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2017). Automatic quantification of tumour hypoxia from multi-modal microscopy images using weakly-supervised learning methods. IEEE Transactions on Medical Imaging, 36(7), 1405-1417.
DOI Scopus4 WoS4 Europe PMC3
2016 Lu, Z., Carneiro, G., Dhungel, N., & Bradley, A. P. (2016). Automated Detection of Individual Micro-calcifications from Mammograms
using a Multi-stage Cascade Approach.
2015 Vochin, M., Borcoci, E., & Carneiro, G. (2015). Media aware network element data plane performance evaluation. UPB Scientific Bulletin Series C Electrical Engineering and Computer Science, 77(3), 77-84.
2015 Lu, Z., Carneiro, G., & Bradley, A. (2015). An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4), 1261-1272.
DOI Scopus229 WoS194 Europe PMC66
2014 Iorga, R., Borcoci, E., Miruta, R., Pinto, A., Carneiro, G., & Calcada, T. (2014). Management driven hybrid multicast framework for content aware networks. IEEE Communications Magazine, 52(1), 158-165.
DOI Scopus1 WoS1
2013 Carneiro, G., & Nascimento, J. (2013). Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data.. IEEE transactions on pattern analysis and machine intelligence.
2013 Carneiro, G., & Nascimento, J. (2013). Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2592-2607.
DOI Scopus124 WoS100 Europe PMC47
2013 Carneiro, G. (2013). Artistic image analysis using graph-based learning approaches. IEEE Transactions on Image Processing, 22(8), 3168-3178.
DOI Scopus5 WoS4
2013 Carneiro, G. L., Braz, D., de Jesus, E. F., Santos, S. M., Cardoso, K., Hecht, A. A., & da Cunha, M. K. D. (2013). Radon in indoor concentrations and indoor concentrations of metal dust particles in museums and other public buildings. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH, 35(3), 333-340.
DOI WoS6
2012 Carneiro, G., Nascimento, J., & Freitas, A. (2012). The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Transactions on Image Processing, 21(3), 968-982.
DOI Scopus187 WoS162 Europe PMC77
2012 Carneiro, G., Fortuna, P., Dias, J., & Ricardo, M. (2012). Transparent and scalable terminal mobility for vehicular networks. Computer Networks, 56(2), 577-597.
DOI Scopus2 WoS1
2012 Del Monego, H., Carneiro, G., Oliveira, J. M., & Ricardo, M. (2012). An ns-3 architecture for simulating joint radio resource management strategies in interconnected WLAN and UMTS networks. Transactions on Emerging Telecommunications Technologies, 23(6), 537-549.
DOI Scopus1 WoS1
2011 Carneiro, G., Fontes, H., & Ricardo, M. (2011). Fast prototyping of network protocols through ns-3 simulation model reuse. Simulation Modelling Practice and Theory, 19(9), 2063-2075.
DOI Scopus21 WoS14
2009 Wels, M., Zheng, Y., Carneiro, G., Huber, M., Hornegger, J., & Comaniciu, D. (2009). Fast and robust 3-D MRI brain structure segmentation. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5762 LNCS(PART 2), 575-583.
DOI Scopus19 Europe PMC6
2009 Carneiro, G., & Vasconcelos, N. (2009). Minimum Bayes error features for visual recognition. Image and Vision Computing, 27(1-2), 131-140.
DOI Scopus2 WoS2
2009 Carneiro, G., & Jepson, A. (2009). The quantitative characterization of the distinctiveness and robustness of local image descriptors. Image and Vision Computing, 27(8), 1143-1156.
DOI Scopus11 WoS10
2009 Zalud, I., Good, S., Carneiro, G., Georgescu, B., Aoki, K., Green, L., . . . Okumura, R. (2009). Fetal biometry: a comparison between experienced sonographers and automated measurements. The Journal of Maternal - Fetal & Neonatal Medicine, 22(1), 43-50.
DOI Scopus15 WoS16 Europe PMC11
2008 Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008). Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Transactions on Medical Imaging, 27(9), 1342-1355.
DOI Scopus187 WoS155 Europe PMC74
2008 Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., & Comaniciu, D. (2008). A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5241 LNCS(PART 1), 67-75.
DOI Scopus72 WoS49 Europe PMC22
2007 Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2007). Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 4792 LNCS(PART 2), 571-579.
DOI Scopus22 Europe PMC5
2007 Carneiro, G., & Ricardo, M. (2007). QoS abstraction layer in 4G access networks. Telecommunication Systems, 35(1-2), 55-65.
DOI Scopus2 WoS1
2007 Carneiro, G., & Jepson, A. (2007). Flexible spatial configuration of local image features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2089-2104.
DOI Scopus50 WoS34 Europe PMC5
2007 Carneiro, G., Chan, A., Moreno, P., & Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 394-410.
DOI Scopus797 WoS532 Europe PMC46
2004 Carneiro, G., Ruela, J., & Ricardo, M. (2004). Cross-layer design in 4G wireless terminals. IEEE Wireless Communications, 11(2), 7-13.
DOI Scopus138 WoS75
1999 Aude, E. P. L., Carneiro, G. H. M. B., Serdeira, H., Silveira, J. T. C., Martins, M. F., & Lopes, E. P. (1999). CONTROLAB MUFA: a multi-level fusion architecture for intelligent navigation of a telerobot. Proceedings IEEE International Conference on Robotics and Automation, 1, 465-472.
Scopus13 WoS11
- Dalva, M., Van Leeuwen Bichara, G. C., Campos Cunha Filho, C. E., Carneiro, G. F., Saliba, G. N., Camacho, J. A., . . . Limaco, R. P. (2009). Intermittent annular reduction with Alfieri's repair in the treatment of mitral insufficiency in children: initial results. REVISTA BRASILEIRA DE CIRURGIA CARDIOVASCULAR, 24(3), 354-358.
DOI WoS6

Year Citation
2024 Carneiro, G. (2024). Machine Learning With Noisy Labels. Elsevier.
DOI Scopus1
2021 Rosen-Zvi, M., Gabrani, M., Konukoglu, E., Beymer, D., Carneiro, G., & Guindy, M. (2021). LL-COVID-19 preface (Vol. 12969 LNCS).
2020 Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., . . . Milford, M. (2020). Semantics for Robotic Mapping, Perception and Interaction: A Survey (Vol. 8). United States: Now Publishers.
DOI
2019 Carneiro, G., & You, S. (2019). Computer Vision – ACCV 2018 Workshops (Vol. 11367 LNCS). G. Carneiro, & S. You (Eds.), Springer.
2019 Carneiro, G., & You, S. (2019). Computer Vision – ACCV 2018 Workshops (Vol. 11367 LNCS). G. Carneiro, & S. You (Eds.), Springer.
2019 Lu, L., Wang, X., Carneiro, G., & Yang, L. (2019). Preface.
2016 Carneiro, G., Tavares, J. M. R. S., Bradley, A., Papa, J. P., Nascimento, J. C., Cardoso, J. S., . . . Lu, Z. (2016). Preface: DLMIA 2016 (Vol. 10008 LNCS).
2016 Mateus, D., Peter, L., Carneiro, G., Loog, M., & Cornebise, J. (2016). Preface: LABELS 2016 (Vol. 10008 LNCS).
2016 Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J. M. R. S., Belagiannis, V., . . . Cornebise, J. (Eds.) (2016). Deep Learning and Data Labeling for Medical Applications. Springer International Publishing.
DOI
2016 Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J. M. R. S., Belagiannis, V., . . . Cornebise, J. (Eds.) (2016). Deep Learning and Data Labeling for Medical Applications. Springer International Publishing.
DOI

Year Citation
2023 Nguyen, C. C., Dawoud, Y., Do, T. -T., Nascimento, J. C., Belagiannis, V., & Carneiro, G. (2023). Smart task design for meta learning medical image analysis systems. In H. V. Nguyen, R. Summers, & R. Chellappa (Eds.), Meta Learning With Medical Imaging and Health Informatics Applications (pp. 185-209). Elsevier.
DOI
2022 Nguyen, C., Dawoud, Y., Do, T. -T., Nascimento, J., Belagiannis, V., & Carneiro, G. (2022). Smart task design for meta learning medical image analysis systems: Unsupervised, weakly-supervised, and cross-domain design of meta learning tasks. In H. V. Nguyen, R. Summers, & R. Chellappa (Eds.), Meta-Learning with Medical Imaging and Health Informatics Applications (pp. 185-209). Elsevier.
DOI Scopus1
2022 Mehta, O., Liao, Z., Jenkinson, M., Carneiro, G., & Verjans, J. (2022). Machine Learning in Medical Imaging - Clinical Applications and Challenges in Computer Vision. In Artificial Intelligence in Medicine: Applications, Limitations and Future Directions (pp. 79-99). Springer Nature Singapore.
DOI Scopus5
2019 Verjans, J., Veldhuis, W. B., Carneiro, G., Wolterink, J. M., Išgum, I., & Leiner, T. (2019). Cardiovascular diseases. In E. R. Ranschaert, S. Morozov, & P. R. Algra (Eds.), Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks (pp. 167-185). Cham, Switzerland: Springer.
DOI Scopus3
2019 Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI. In L. Lu, X. Wang, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (pp. 163-178). Cham, Switzerland: Springer.
DOI Scopus2
2019 Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI. In L. Lu, X. Wang, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (pp. 163-178). Cham, Switzerland: Springer.
DOI Scopus2
2019 Carneiro, G., & You, S. (2019). Preface. In G. Carneiro, & S. You (Eds.), Computer Vision – ACCV 2018 Workshops (Vol. 11367 LNCS, pp. v).
2019 Carneiro, G., & You, S. (2019). Preface. In G. Carneiro, & S. You (Eds.), Computer Vision – ACCV 2018 Workshops (Vol. 11367 LNCS, pp. v).
2018 Carneiro, G., Tavares, J. M. R. S., Bradley, A., Papa, J. P., Belagiannis, V., Nascimento, J. C., . . . Conjeti, S. (2018). DLMIA 2018 Preface. In D. Stoyanov, Z. Taylor, G. Carneiro, & T. Syeda-Mahmood (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 20, 2018 Proceedings (Vol. 11045 LNCS, pp. VII).
2018 Carneiro, G., Tavares, J. M. R. S., Bradley, A., Papa, J. P., Belagiannis, V., Nascimento, J. C., . . . Conjeti, S. (2018). DLMIA 2018 Preface. In D. Stoyanov, Z. Taylor, G. Carneiro, & T. Syeda-Mahmood (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 20, 2018 Proceedings (Vol. 11045 LNCS, pp. VII).
2017 Carneiro, G., Nascimento, J., & Bradley, A. (2017). Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions. In S. Zhou, H. Greenspan, & D. Shen (Eds.), Deep Learning for Medical Image Analysis (pp. 321-339). London: Elsevier.
DOI Scopus39
2017 Carneiro, G., Zheng, Y., Xing, F., & Yang, L. (2017). Review of deep learning methods in mammography, cardiovascular, and microscopy image analysis. In L. Lu, Y. Zheng, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing: precision medicine, high performance and large-scale datasets (pp. 11-32). Switzerland: Springer.
DOI Scopus42
2017 Ngo, T., & Carneiro, G. (2017). Fully automated segmentation using distance regularised level set and deep-structured learning and inference. In L. Lu, Y. Zheng, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing: precision medicine, high performance and large-scale datasets (pp. 197-224). Switzerland: Springer.
DOI Scopus5
2017 Dhungel, N., Carneiro, G., & Bradley, A. (2017). Combining deep learning and structured prediction for segmenting masses in mammograms. In L. Lu, Y. Zheng, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing: precision medicine, high performance and large-scale datasets (pp. 225-240). Switzerland: Springer.
DOI Scopus9
2016 Nascimento, J., Carneiro, G., & Freitas, A. (2016). Tracking and segmentation of the endocardium of the left ventricle in a 2D ultrasound using deep learning architectures and monte carlo sampling. In A. El-Baz, X. Jiang, & J. S. Suri (Eds.), Biomedical Image Segmentation: Advances and Trends (pp. 387-406). Florida; USA: CRC Press.
DOI Scopus2
2015 Chen, Q., & Carneiro, G. (2015). Artistic Image Analysis Using the Composition of Human Figures. In Lecture Notes in Computer Science (pp. 117-132). Springer International Publishing.
DOI

Year Citation
2026 Butler, D., Hilton, A., & Carneiro, G. (2026). Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model Using X-Ray Images. In Lecture Notes in Computer Science Vol. 15973 LNCS (pp. 539-549). Springer Nature Switzerland.
DOI
2025 Vale, W., Bamber, J., Koruk, H., Carneiro, G., & Florescu, L. (2025). Reconstructing the Tissue Absorption Coefficient in Photoacoustic Tomography with Large Scale Simulations: Numerical Experiments with Digimouse. In IEEE International Ultrasonics Symposium Ius (pp. 1-5). IEEE.
DOI
2025 Zhang, Y., Xie, Y., Wang, H., Avery, J. C., Hull, M. L., & Carneiro, G. (2025). A Novel Perspective for Multi-Modal Multi-Label Skin Lesion Classification. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 3549-3558). Tucson, AZ, USA: IEEE.
DOI
2025 Vale, W., Bamber, J., Koruk, H., Carneiro, G., & Florescu, L. (2025). Deep Learning for Photoacoustic Imaging of CAR-T Cells in Cancer Immunotherapy. In C. Boehm, & M. Mehrmohammadi (Eds.), Progress in Biomedical Optics and Imaging Proceedings of SPIE Vol. 13412 (pp. 6 pages). CA, San Diego: SPIE-INT SOC OPTICAL ENGINEERING.
DOI
2025 Nguyen, C., Do, T. T., & Carneiro, G. (2025). PROBABILISTIC LEARNING TO DEFER: HANDLING MISSING EXPERT'S ANNOTATIONS AND CONTROLLING WORKLOAD DISTRIBUTION. In 13th International Conference on Learning Representations Iclr 2025 (pp. 101983-102003).
2025 Pitawela, D., Carneiro, G., & Chen, H. T. (2025). CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 15538-15548). IEEE.
DOI Scopus1
2025 Garg, A., Nguyen, C., Felix, R., Do, T. -T., & Carneiro, G. (2025). Instance-Dependent Noisy-Label Learning with Graphical Model Based Noise-Rate Estimation. In Lecture Notes in computer science Vol. 15062 LNCS (pp. 372-389). Milan, Italy: Springer Nature Switzerland.
DOI
2025 Liu, Y., Chen, Y., Wang, H., Belagiannis, V., Reid, I., & Carneiro, G. (2025). ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation. In Proceedings of the 18th European Conference on Computer Vision (ECCV, 2024) Vol. 15059 (pp. 81-99). Milan, Italy: Springer Science and Business Media Deutschland GmbH.
DOI
2025 Zhang, Z., Ai, W., Wells, K., Rosewarne, D., Do, T. T., & Carneiro, G. (2025). Learning to Complement and to Defer to Multiple Users. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 15114 LNCS (pp. 144-162). ITALY, Milan: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus5 WoS1
2025 Tapper, W., Carneiro, G., Hussein, M., Evans, P., & Thomas, S. A. (2025). Effects of Primary Capsule Shapes and Sizes in Capsule Networks. In A. Antonacopoulos, S. Chaudhuri, R. Chellappa, C. L. Liu, S. Bhattacharya, & U. Pal (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 15305 LNCS (pp. 141-158). INDIA, Kolkata: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus1
2025 Tan, Z. Q., Isupova, O., Carneiro, G., Zhu, X., & Li, Y. (2025). Bayesian Detector Combination for Object Detection with Crowdsourced Annotations. In Proceedings of the 18th European Conference on Computer Vision, Part LXIII (ECCV 2024), as published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15121 LNCS (pp. 329-346). Cham, Switzerland: Springer Nature.
DOI
2024 Pham, C., Hoang, A. D., Nguyen, C. C., Le, T., Phung, D., Carneiro, G., & Do, T. -T. (2024). MetaAug: Meta-data Augmentation for Post-training Quantization. In Proceedings of the 18th The 18th European Conference on Computer Vision ECCV (2024) as published in Computer Vision Vol. 15085 (pp. 236-252). Milan, Italy: Springer Nature Switzerland.
DOI
2024 Chen, Y., Wang, C., Liu, Y., Wang, H., & Carneiro, G. (2024). CPM: Class-Conditional Prompting Machine for Audio-Visual Segmentation. In Lecture Notes in Computer Sciences Vol. 15068 LNCS (pp. 438-456). Milan, Italy: Springer Nature Switzerland.
DOI Scopus1
2024 Petashvili, D., Wang, H., Deslandes, A., Avery, J., Condous, G., Carneiro, G., . . . Chen, H. T. (2024). Learning Subjective Image Quality Assessment for Transvaginal Ultrasound Scans from Multi-Annotator Labels. In Proceedings - International Symposium on Biomedical Imaging (pp. 5 pages). Online: IEEE.
DOI Scopus3
2024 Chen, Y., Liu, F., Wang, H., Wang, C., Liu, Y., Tian, Y., & Carneiro, G. (2024). BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV, 2023) (pp. 21227-21238). Online: IEEE.
DOI Scopus12 WoS5
2024 Liu, Y., Ding, C., Tian, Y., Pang, G., Belagiannis, V., Reid, I., & Carneiro, G. (2024). Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 1151-1161). online: IEEE.
DOI Scopus29 WoS21
2024 Pham, C., Nguyen, V. A., Le, T., Phung, D., Carneiro, G., & Do, T. T. (2024). Frequency Attention for Knowledge Distillation. In Proceedings 2024 IEEE Winter Conference on Applications of Computer Vision Wacv 2024 (pp. 2266-2275). HI, Waikoloa: IEEE COMPUTER SOC.
DOI Scopus32 WoS28
2024 Bijlani, N., Yin, M., Carneiro, G., Barnaghi, P., & Kouchaki, S. (2024). Computer vision-inspired contrastive learning for self-supervised anomaly detection in sensor-based remote healthcare monitoring. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS Vol. 2024 (pp. 1-5). United States: IEEE.
DOI Scopus2
2024 Chen, Y., Liu, Y., Wang, H., Liu, F., Wang, C., Frazer, H., & Carneiro, G. (2024). Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2024) (pp. 26487-26497). Seattle, WA, USA: Institute of Electrical and Electronics Engineers (IEEE).
DOI Scopus12
2024 El-Ghoussani, A., Hornauer, J., Carneiro, G., & Belagiannis, V. (2024). CONSISTENCY REGULARISATION FOR UNSUPERVISED DOMAIN ADAPTATION IN MONOCULAR DEPTH ESTIMATION. In Proceedings of Machine Learning Research Vol. 274 (pp. 577-596).
2023 Wang, C., Liu, Y., Chen, Y., Liu, F., Tian, Y., McCarthy, D., . . . Carneiro, G. (2023). Learning Support and Trivial Prototypes for Interpretable Image Classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2023) (pp. 2062-2072). Paris, France: IEEE.
DOI Scopus27 WoS17
2023 Leonidas, G., Carneiro, G., Peixoto, R., Britto, D., Neto, J., & Frota, I. (2023). SEXUAL BEHAVIORS AND SOCIAL BARRIERS TO ACCESS PUBLIC HEALTH OF BRAZILIAN TRANSGENDER MEN. In JOURNAL OF SEXUAL MEDICINE Vol. 20 (pp. 1 page). OXFORD UNIV PRESS.
2023 Dawoud, Y., Carneiro, G., & Belagiannis, V. (2023). SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023) (pp. 973-982). Paris, France: IEEE.
DOI Scopus3 WoS3
2023 Pham, C., Nguyen, C. C., Le, T., Phung, D., Carneiro, G., & Do, T. T. (2023). Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. In Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS, 2023) as published in Advances in Neural Information Processing Systems Vol. 36 (pp. 1-11). Online: Neural Information Processing Systems Foundation.
Scopus3
2023 Wang, H., Ma, C., Zhang, J., Zhang, Y., Avery, J., Hull, L., & Carneiro, G. (2023). Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14223 (pp. 216-226). Vancouver, BC, Canada: Springer Nature Switzerland.
DOI Scopus33 WoS20
2023 Smart, B., & Carneiro, G. (2023). Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 5333-5343). Waikoloa, HI, USA: IEEE.
DOI Scopus14 WoS11
2023 Dawoud, Y., Bouazizi, A., Ernst, K., Carneiro, G., & Belagiannis, V. (2023). Knowing What to Label for Few Shot Microscopy Image Cell Segmentation. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 3557-3566). Waikoloa, HI, USA: IEEE.
DOI Scopus6 WoS1
2023 Hull, M. L., Wang, H., Zhang, Y., Avery, J., To, M. S., Carneiro, G., & Butler, D. (2023). The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification.. In Proceedings of the 45th IEEE Engineering in Medicine and Biology Society Vol. 2023 (pp. 5 pages). Online: IEEE.
DOI Scopus5 WoS6
2023 Butler, D., Wang, H., Zhang, Y., To, M. S., Avery, J. C., Hull, M. L., & Carneiro, G. (2023). The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification. In Proceedings of 45th IEEE Engineering in Medicine and Biology Society. Online: IEEE.
DOI
2023 Wang, H., Chen, Y., Ma, C., Avery, J. C., Hull, M. L., & Carneiro, G. (2023). Multi-Modal Learning With Missing Modality via Shared-Specific Feature Modelling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023) Vol. 2023-June (pp. 15878-15887). Vancouver, Canada: IEEE.
DOI Scopus139 WoS94
2023 Zhang, Y., Wang, H., Butler, D., To, M. -S., Avery, J. C., Hull, M. L., & Carneiro, G. (2023). Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images. In Proceedings of the IEEE 20th International Symposium on Biomedical Imaging (ISBI 2023) Vol. 2023-April (pp. 1-5). Cartagena de Indias, Colombia: IEEE.
DOI Scopus9 WoS7
2023 Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2023). Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder. In Proceedings of the 14th International Workshop, Machine Learning in Medical Imaging (MLMI 2023), as published in Lecture Notes in Computer Science Vol. 14349 (pp. 11-21). Cham, Switzerland: Springer Nature.
DOI Scopus5 WoS11
2023 Garg, A., Nguyen, C., Felix, R., Do, T. -T., & Carneiro, G. (2023). Instance-Dependent Noisy Label Learning via Graphical Modelling. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 2287-2297). Online: IEEE.
DOI Scopus34 WoS31
2023 Galdran, A., Carneiro, G., & Ballester, M. A. G. (2023). On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness. In Proceedings of the 3rd Diabetic Foot Ulcer Challenge (DFUC 2022), as published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13797 LNCS (pp. 40-51). Cham, Switzerland: Springer.
DOI Scopus17
2022 Buris, L. H., Pedronette, D. C. G., Papa, J. P., Almeida, J., Carneiro, G., & Faria, F. A. (2022). MIXUP-BASED DEEP METRIC LEARNING APPROACHES FOR INCOMPLETE SUPERVISION. In Proceedings International Conference on Image Processing Icip (pp. 2581-2585). FRANCE, Bordeaux: IEEE.
DOI Scopus2 WoS1
2022 Chen, Y., Tian, Y., Pang, G., & Carneiro, G. (2022). Deep One-Class Classification via Interpolated Gaussian Descriptor. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 Vol. 36 (pp. 383-392). Online: Association for the Advancement of Artificial Intelligence.
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2022 Galdran, A., Hewitt, K. J., Ghaffari Laleh, N., Kather, J. N., Carneiro, G., & González Ballester, M. A. (2022). Test Time Transform Prediction for Open Set Histopathological Image Recognition. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13432 (pp. 263-272). Singapore: Springer.
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2022 Pereira, E., Carneiro, G., & Cordeiro, F. R. (2022). A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels. In B. M. DeCarvalho, & L. M. G. Goncalves (Eds.), Proceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022 (pp. 25-30). BRAZIL, Fed Univ Rio Grande Norte, Natal: IEEE.
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2022 Liu, F., Tian, Y., Chen, Y., Liu, Y., Belagiannis, V., & Carneiro, G. (2022). ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2022) Vol. 2022-June (pp. 20665-20674). New Orleans, Louisiana: IEEE.
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2022 Liu, Y., Tian, Y., Chen, Y., Liu, F., Belagiannis, V., & Carneiro, G. (2022). Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2022) Vol. 2022-June (pp. 4248-4257). New Orleans, Louisiana: IEEE.
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2022 Tian, Y., Liu, Y., Pang, G., Liu, F., Chen, Y., & Carneiro, G. (2022). Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes. In Proceedings, Part XXXIX of the 17th European Conference on Computer Vision (ECCV 2022), as published in Lecture Notes in Computer Science Vol. 13699 LNCS (pp. 246-263). Cham, Switzerland: Springer.
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2022 Wang, H., Zhang, J., Chen, Y., Ma, C., Avery, J., Hull, L., & Carneiro, G. (2022). Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13697 LNCS (pp. 200-217). Online: Springer Nature Switzerland.
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2022 Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., . . . Carneiro, G. (2022). Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 88-98). Online: Springer Link.
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2022 Galdran, A., Carneiro, G., & Ballester, M. A. G. (2022). Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification. In Proceedings of the 2nd Diabetic Foot Ulcers Grand Challenge (DFUC 2021), as published in Lecture Notes in Computer Science Vol. 13183 LNCS (pp. 21-29). Cham, Switzerland: Springer International Publishing.
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2022 Hermoza, R., Maicas, G., Nascimento, J. C., & Carneiro, G. (2022). Censor-Aware Semi-supervised Learning for Survival Time Prediction from Medical Images. In Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13437 (pp. 213-222). Singapore: Springer.
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2022 Liu, F., Chen, Y., Tian, Y., Liu, Y., Wang, C., Belagiannis, V., & Carneiro, G. (2022). NVUM: Non-volatile Unbiased Memory for Robust Medical Image Classification. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 544-553). Singapore: Springer.
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2022 Wang, C., Chen, Y., Liu, Y., Tian, Y., Liu, F., McCarthy, D. J., . . . Carneiro, G. (2022). Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 14-24). Singapore: Springer.
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2022 Tan, J. L., Chinnaratha, M. A., Woodman, R., Martin, R., Chen, H. -T., Carneiro, G., & Singh, R. (2022). Diagnostic accuracy of artificial intelligence to detect early neoplasia in Barrett's esophagus: A non-comparative systematic review and meta-analysis. In JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY Vol. 37 (pp. 21). WILEY.
2022 Chen, Y., Wang, H., Wang, C., Tian, Y., Liu, F., Liu, Y., . . . Carneiro, G. (2022). Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13433 LNCS (pp. 3-13). Online: Springer.
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2022 Dawoud, Y., Ernst, K., Carneiro, G., & Belagiannis, V. (2022). Edge-Based Self-supervision for Semi-supervised Few-Shot Microscopy Image Cell Segmentation. In Proceedings of the1st International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis (MOVI 2022), Held in Conjunction with MICCAI 2022, as published in Lecture Notes in Computer Science Vol. 13578 (pp. 22-31). Singapore: Springer.
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2022 Snaauw, G., Sasdelli, M., Maicas, G., Lau, S., Verjans, J., Jenkinson, M., & Carneiro, G. (2022). Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images.. In EMBC (pp. 3510-3513). IEEE.
2022 Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., . . . Carneiro, G. (2022). Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), MICCAI (3) Vol. 13433 (pp. 88-98). Springer.
2022 Snaauw, G., Sasdelli, M., Maicas, G., Lau, S., Verjans, J., Jenkinson, M., & Carneiro, G. (2022). Mutual information neural estimation for unsupervised multi-modal registration of brain images. In 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2022-July (pp. 3510-3513). Online: IEEE.
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2022 Butler, D., Zhang, Y., Chen, T., Shin, S. H., Singh, R., & Carneiro, G. (2022). In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos. In Proceedings of the 19th IEEE International Symposium on Biomedical Imaging (ISBI 2022)) Vol. 2022 (pp. 5 pages). Online: IEEE.
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2022 Pham, R., Marshall, H., Kolovos, A., Qassim, A., Mullany, S., Saks, D., . . . Palmer, L. J. (2022). Associations between deep learning segmented macular optical coherence tomography cell layer thicknesses and primary open-angle glaucoma outcomes in the PROGRESSA study. In CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY Vol. 49 (pp. 913-914). WILEY.
2021 Gabrani, M., Konukoglu, E., Beymer, D., Carneiro, G., Born, J., Guindy, M., & Rosen-Zvi, M. (2021). Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next. In Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning Vol. 12969 LNCS (pp. 133-140). Switzerland: Springer International Publishing.
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2021 Sasdelli, M., Ajanthan, T., Chin, T. J., & Carneiro, G. (2021). A Chaos Theory Approach to Understand Neural Network Optimization. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021) (pp. 1-10). online: IEEE.
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2021 Le, H. S., Akmeliawati, R., & Carneiro, G. (2021). Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021) (pp. 1-8). online: IEEE.
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2021 Galdran, A., Carneiro, G., & González Ballester, M. A. (2021). Multi-center polyp segmentation with double encoder-decoder networks. In Proceedings of the 18th IEEE International Symposium on Biomedical Imaging (ISBI2021) Vol. 2886 (pp. 9-16). online: IEEE.
2021 Ferreira, Á. R., de Rosa, G. H., Papa, J. P., Carneiro, G., & Faria, F. A. (2021). Creating classifier ensembles through meta-heuristic algorithms for aerial scene classification. In Proceedings - International Conference on Pattern Recognition (pp. 415-422). online: IEEE.
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2021 Sachdeva, R., Cordeiro, F. R., Belagiannis, V., Reid, I., & Carneiro, G. (2021). EvidentialMix: learning with combined open-set and closed-set noisy labels. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3606-3614). Headquarters, New York, NY, USA: IEEE.
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2021 Jonmohamadi, Y., Ali, S., Liu, F., Roberts, J., Crawford, R., Carneiro, G., & Pandey, A. K. (2021). 3D semantic mapping from arthroscopy using out-of-distribution pose and depth and in-distribution segmentation training. In Procedeengs of the International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 12902 LNCS (pp. 383-393). Switzerland: Springer International Publishing.
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2021 Liu, F., Tian, Y., Cordeiro, F. R., Belagiannis, V., Reid, I., & Carneiro, G. (2021). Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification. In Proceedings of the 12th International Workshop, Machine Learning in Medical Imaging (MLMI 2021), as published in Lecture Notes in Computer Science Vol. 12966 LNIP (pp. 426-436). Switzerland: Springer International Publishing.
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2021 Galdran, A., Carneiro, G., & González Ballester, M. A. (2021). Balanced-MixUp for highly imbalanced medical image classification. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, as published in Lecture Notes in Computer Science Vol. 12905 (pp. 323-333). Strasbourg: Springer International Publishing.
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2021 To, M. S., Sarno, I. G., Chong, C., Jenkinson, M., & Carneiro, G. (2021). Self-supervised lesion change detection and localisation in longitudinal multiple sclerosis brain imaging. In Proceedings of the 24th International Conference on Medical Image Computing and Computer - Assisted Intervention as published in Image Processing, Computer Vision, Pattern Recognition, and Graphics Vol. 12907 LNCS (pp. 670-680). online: Springer International Publishing.
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2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) Vol. abs/2101.10030 (pp. 4955-4966). virtual online: IEEE.
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2021 Nguyen, C., Do, T. -T., & Carneiro, G. (2021). Similarity of Classification Tasks.
2021 Hermoza Aragones, R., Maicas Suso, G., Nascimento, J. C., & Carneiro, G. (2021). Post-hoc overall survival time prediction from brain MRI. In Proceedings of the IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021) Vol. 2021-April (pp. 1476-1480). online: IEEE.
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2021 Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images. In Proceedings of the 24th Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), as publisghed in Lecture Notes in Computer Science Vol. 12905 (pp. 128-140). Cham, Switzerland: Springer.
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2021 Dawoud, Y., Hornauer, J., Carneiro, G., & Belagiannis, V. (2021). Few-shot microscopy image cell segmentation. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discover in Databases (ECML PKDD 2020), as published in Lecture Notes in Computer Science Vol. 12461 (pp. 139-154). Cham, Switzerland: Springer.
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2021 Galdran, A., Carneiro, G., & Ballester, M. A. G. (2021). A Hierarchical Multi-task Approach to Gastrointestinal Image Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12668 LNCS (pp. 275-282). Switzerland: Springer International Publishing.
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2021 Galdran, A., Carneiro, G., & Ballester, M. A. G. (2021). Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12661 LNCS (pp. 293-307). Switzerland: Springer International Publishing.
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2021 Nguyen, C. C., Do, T. -T., & Carneiro, G. (2021). Probabilistic task modelling for Meta-Learning. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2021), as published in Proceedings of Machine Learning Research Vol. 161 (pp. 781-791). Vancouver BC Canada: Association For Uncertainty in Artificial Intelligence (AUAI).
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2021 Le, H. S., Akmeliawati, R., & Carneiro, G. (2021). Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract). In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21) Vol. 35 (pp. 15821-15822). Palo Alto, California, USA: AAAI Press.
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2021 Cordeiro, F. R., Belagiannis, V., Reid, I., & Carneiro, G. (2021). PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels. In 32nd British Machine Vision Conference, BMVC 2021 (pp. 1-16). Online: BMVC.
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2021 Le, H. -S., Akmeliawati, R., & Carneiro, G. (2021). Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error.. In J. Zhou, O. Salvado, F. Sohel, P. Borges, & S. Wang (Eds.), DICTA (pp. 1-8). Gold Coast, Australia: IEEE.
2021 Sasdelli, M., Ajanthan, T., Chin, T. -J., & Carneiro, G. (2021). A Chaos Theory Approach to Understand Neural Network Optimization.. In J. Zhou, O. Salvado, F. Sohel, P. Borges, & S. Wang (Eds.), DICTA (pp. 1-10). IEEE.
2021 Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images.. In M. D. Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), MICCAI (5) Vol. 12905 (pp. 128-140). Springer.
2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning.. In ICCV (pp. 4955-4966). IEEE.
2021 Nguyen, C. C., Thanh-Toan, D., & Carneiro, G. (2021). Probabilistic Task Modelling for Meta-Learning. In C. DeCampos, & M. H. Maathuis (Eds.), UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, VOL 161 Vol. 161 (pp. 781-791). Toronto, Canada: JMLR-JOURNAL MACHINE LEARNING RESEARCH.
2020 Nguyen, B. X., Nguyen, B. D., Carneiro, G., Tjiputra, E., Tran, Q. D., & Do, T. T. (2020). Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding. In 31st British Machine Vision Conference Bmvc 2020.
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2020 Liu, F., Jonmohamadi, Y., Maicas Suso, G., Pandey, A. K., & Carneiro, G. (2020). Self-supervised depth estimation to regularise semantic segmentation in knee arthroscopy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12261 LNCS (pp. 594-603). Switzerland: Springer Nature.
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2020 Johnston, A., & Carneiro, G. (2020). Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2020) (pp. 4755-4764). online: IEEE.
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2020 Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Ré, C. (2020). Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.. In CHIL '20: Proceedings of the ACM Conference on Health, Inference, and Learning Vol. abs/1909.12475 (pp. 151-159). New York, NY, United States: Association for Computing Machinery.
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2020 Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Ré, C. (2020). Hidden stratification causes clinically meaningful failures in machine learning for medical imaging.. In M. Ghassemi (Ed.), Proceedings of the ACM Conference on Health, Inference, and Learning (CHIL) (pp. 151-159). online: ACM.
2020 Hermoza Aragones, R., Maicas Suso, G., Nascimento, J. C., & Carneiro, G. (2020). Region proposals for saliency map refinement for weakly-supervised disease localisation and classification. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 23rd International Conference, Lima, Peru, October 4–8, 2020. Proceedings, Part VI Vol. 12266 LNCS (pp. 539-549). Switzerland: Springer Nature.
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2020 Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., . . . Sunderhauf, N. (2020). Probabilistic object detection: Definition and evaluation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1020-1029). online: IEEE.
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2020 Liu, Y., Tian, Y., Maicas Suso, G., Zorron Cheng Tao Pu, L., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Photoshopping colonoscopy video frames. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1-5). Iowa City, Iowa, USA: IEEE.
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2020 Glaser, S., Maicas Suso, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2020). Semi-supervised multi-domain multi-task training for metastatic colon lymph node diagnosis from abdominal CT. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1478-1481). Iowa City, Iowa, USA: IEEE.
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2020 Tian, Y., Maicas Suso, G., Zorron Cheng Tao Pu, L., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Few-shot anomaly detection for polyp frames from colonoscopy. In Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020), as published in Lecture Notes in Computer Science Vol. 12266 (pp. 274-284). Switzerland: Springer Nature.
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2020 Cordeiro, F. R., & Carneiro, G. (2020). A survey on deep learning with noisy labels: How to train your model when you cannot trust on the annotations?. In Proceedings of the 33rd Conference on Graphics, Patterns and Images (SIBGRAPI 2020) (pp. 9-16). online: IEEE.
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2020 Faria, F. A., & Carneiro, G. (2020). Why are generative adversarial networks so fascinating and annoying?. In Proceedings of the 33rd Conference on Graphics, Patterns and Images (SIBGRAPI 2020) (pp. 1-8). online: IEEE.
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2020 Felix Alves, R., Sasdelli, M., Reid, I. D., & Carneiro, G. (2020). Augmentation network for generalised zero-shot learning. In H. Ishikawa, C. -L. Liu, T. Pajdla, & J. Shi (Eds.), Proceedings of the 15th Asian Conference on Computer Vision (ACCV 2020), as published in Lecture Notes in Computer Science Vol. 12625 (pp. 442-458). Cham, Switzerland: Springer.
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2020 Felix, R., Sasdelli, M., Harwood, B., & Carneiro, G. (2020). Generalised Zero-shot Learning with Multi-modal Embedding Spaces. In Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2020) (pp. 1-8). online: IEEE.
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2020 Maicas, G., Nguyen, C., Motlagh, F., Nascimento, J. C., & Carneiro, G. (2020). Unsupervised task design to meta-train medical image classifiers. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1339-1342). Iowa City, Iowa, USA: IEEE.
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2020 Nguyen, C., Do, T. T., & Carneiro, G. (2020). Uncertainty in model-agnostic meta-learning using variational inference. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2020) (pp. 3079-3089). online: IEEE.
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2019 Pu, L. Z. C. T., Maicas, G., Tian, Y., Yamamura, T., Singh, G., Rana, K., . . . Singh, R. (2019). Prospective study assessing a comprehensive computer-aided diagnosis for characterization of colorectal lesions: Results from different centers and imaging technologies. In JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY Vol. 34 (pp. 25-26). WILEY.
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2019 Maicas Suso, G., Snaauw, G., Bradley, A. P., Reid, I., & Carneiro, G. (2019). Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI. In Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Vol. 2019-April (pp. 1057-1060). online: IEEE.
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2019 Tian, Y., Pu, L. Z. C. T., Singh, R., Burt, A. D., & Carneiro, G. (2019). One-stage five-class polyp detection and. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI) Vol. 2019-April (pp. 70-73). online: IEEE.
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2019 Gale, W., Oakden-Rayner, L., Carneiro, G., Palmer, L. J., & Bradley, A. P. (2019). Producing radiologist quality reports for interpretable deep learning. In Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging Vol. 2019-April (pp. 1275-1279). online: IEEE.
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2019 Snaauw, G., Gong, D., Maicas, G., van den Hengel, A., Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): Proceedings Vol. 2019-April (pp. 802-805). online: IEEE.
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2019 Felix Alves, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised zero-shot learning with domain classification in a joint semantic and visual space. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2019) (pp. 1-8). online: IEEE.
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2019 Tran, T. M., Do, T. -T., Reid, I., & Carneiro, G. (2019). Bayesian generative active deep learning. In K. Chaudhuri, & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (IMCL), as published in Proceedings of Machine Learning Research Vol. 97 (pp. 6295-6304). Long Beach, CA, USA: PMLR.
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2019 Johnston, A., & Carneiro, G. (2019). Single view 3D point cloud reconstruction using novel view synthesis and self-supervised depth estimation. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2019) (pp. 1-8). online: IEEE.
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2019 Do, T. -T., Tran, T., Reid, I., Kumar, V., Hoang, T., & Carneiro, G. (2019). A theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019) Vol. 2019-June (pp. 10396-10405). Long Beach, CA, USA: IEEE.
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2019 Tran, T., Do, T. T., Reid, I., & Carneiro, G. (2019). Bayesian Generative Active Deep Learning. In Proceedings of Machine Learning Research Vol. 97 (pp. 6295-6304).
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2018 Camps, S., Houben, T., Edwards, C., Antico, M., Dunnhofer, M., Martens, E., . . . Fontanarosa, D. (2018). Quality assessment of transperineal ultrasound images of the male pelvic region using deep learning. In Proceedings of the IEEE International Ultrasonics Symposium as plubished in IEEE Xplore Vol. 2018 (pp. 1-4). online: IEEE.
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2018 Maicas Suso, G., Bradley, A., Nascimento, J., Reid, I., & Carneiro, G. (2018). Training medical image analysis systems like radiologists. In A. Frangi, J. Schnabel, C. Davatzikos, C. Alberola-Lopez, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018: proceedings, part 1 Vol. 11070 LNCS (pp. 546-554). Granada: Springer.
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2018 Felix, R., Vijay Kumar, B., Reid, I., & Carneiro, G. (2018). Multi-modal cycle-consistent generalized zero-shot learning. In Computer Vision - ECCV 2018: proceedings, part VI Vol. 11210 LNCS (pp. 21-37). Munich: Springer.
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2018 Pham, T., Vijay Kumar, B., Do, T., Carneiro, G., & Reid, I. (2018). Bayesian semantic instance segmentation in open set world. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018 15th European Conference: Proceedings, Part I Vol. 11214 LNCS (pp. 3-18). Munich: Springer.
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2018 Cardoso, J., Marques, N., Dhungel, N., Carneiro, G., & Bradley, A. (2018). Mass segmentation in mammograms: a cross-sensor comparison of deep and tailored features. In Proceedings of the 24th IEEE International Conference on Image Processing (ICIP 2017) Vol. 2017 (pp. 1737-1741). NJ, USA: IEEE.
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2018 Pu, L. Z. C. T., Campbell, B., Burt, A. D., Carneiro, G., & Singh, R. (2018). COMPUTER-AIDED DIAGNOSIS FOR CHARACTERISING COLORECTAL LESIONS: INTERIM RESULTS OF A NEWLY DEVELOPED SOFTWARE. In GASTROINTESTINAL ENDOSCOPY Vol. 87 (pp. AB245). Washington, DC: MOSBY-ELSEVIER.
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2018 Pu, L. Z. C. T., Lu, K., Ovenden, A., Edwards, S., Wilson, B., Carneiro, G., . . . Singh, R. (2018). EFFECT OF TRAINING AND TIME OF THE DAY ON POLYP DETECTION RATES IN AUSTRALIA. In GASTROINTESTINAL ENDOSCOPY Vol. 87 (pp. AB161). Washington, DC: MOSBY-ELSEVIER.
2018 Tran, T., Pham, T., Carneiro, G., Palmer, L., & Reid, I. (2018). A Bayesian data augmentation approach for learning deep models. In Advances in Neural Information Processing Systems 30 (NIPS 2017) Vol. 2017-December (pp. 1-10). Long Beach, CA: Neural Information Processing Systems Foundation.
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2017 Johnston, A., Garg, R., Carneiro, G., Reid, I., & van den Hengel, A. (2017). Scaling CNNs for high resolution volumetric reconstruction from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW 2017) Vol. 2018-January (pp. 930-939). Piscataway, NJ: IEEE.
DOI Scopus35 WoS23
2017 Maicas, G., Carneiro, G., Bradley, A., Nascimento, J., & Reid, I. (2017). Deep reinforcement learning for active breast lesion detection from DCE-MRI. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, & S. Duchesne (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2017: 20th International Conference. Proceedings, Part III Vol. 10435 LNCS (pp. 665-673). Quebec City, Canada: Springer.
DOI Scopus97
2017 Pu, L. Z. C. T., Campbell, B., Carneiro, G., Burt, A. D., & Singh, R. (2017). Computer-aided diagnosis (CAD) for characterizing colorectal lesions: Initial results of newly developed software. In JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY Vol. 32 (pp. 34-35). WILEY.
2017 Williams, J., Carneiro, G., & Suter, D. (2017). Region of interest autoencoders with an application to pedestrian detection. In Y. Guo, H. Li, W. Cai, M. Murshed, Z. Wang, J. Gao, & D. Feng (Eds.), Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017) Vol. 2017-December (pp. 580-587). Piscataway, NJ: IEEE.
DOI
2017 Dhungel, N., Carneiro, G., & Bradley, A. (2017). Fully automated classification of mammograms using deep residual neural networks. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 310-314). Online: IEEE.
DOI Scopus89 WoS58
2017 Maicas, G., Carneiro, G., & Bradley, A. (2017). Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 305-309). Online: IEEE.
DOI Scopus28 WoS25
2017 Harwood, B., Kumar, V., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart mining for deep metric learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017-October (pp. 2840-2848). Piscataway, NJ: IEEE.
DOI Scopus245 WoS239
2017 Ribeiro, D., Carneiro, G., Nascimento, J., & Bernardino, A. (2017). Multi-channel convolutional neural network ensemble for pedestrian detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10255 LNCS (pp. 122-130). Faro, Portugal: Springer.
DOI Scopus3 WoS3
2017 Carneiro, G., Oakden-Rayner, L., Bradley, A., Nascimento, J., & Palmer, L. (2017). Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Vol. abs/1607.00267 (pp. 130-134). Online: IEEE.
DOI Scopus20 WoS12
2016 Dhungel, N., Carneiro, G., & Bradley, A. (2016). The automated learning of deep features for breast mass classification from mammograms. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II - MICCAI 2016 Vol. 9901 (pp. 106-114). Athens, Greece: Springer.
DOI Scopus134
2016 Lee, H., Weerasinghe, A., Barnes, J., Oakden-Rayner, L., Gale, W., & Carneiro, G. (2016). CRISTAL: adapting workplace training to the real world context with an intelligent simulator for radiology trainees. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems Vol. 9684 (pp. 430-435). Zagreb, Croaria: Springer.
DOI Scopus3 WoS3
2016 Liao, Z., & Carneiro, G. (2016). On the importance of normalisation layers in deep learning with piecewise linear activation units. In Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (pp. 1-8). Lake Placid, NY: IEEE.
DOI Scopus39 WoS22
2016 Nascimento, J., & Carneiro, G. (2016). Multi-atlas segmentation using manifold learning with deep belief networks. In Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging Vol. 2016-June (pp. 867-871). Prague, Czech Republic: IEEE.
DOI Scopus12 WoS8
2016 Vijay Kumar, B., Carneiro, G., & Reid, I. (2016). Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 5385-5394). Las Vegas, NV: IEEE.
DOI Scopus277 WoS203
2016 Garg, R., Vijay Kumar, B., Carneiro, G., & Reid, I. (2016). Unsupervised CNN for single view depth estimation: geometry to the rescue. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9912 LNCS (pp. 740-756). Amsterdam, Netherlands: Springer International Publishing.
DOI Scopus1167 WoS1162
2015 Dhungel, N., Carneiro, G., & Bradley, A. (2015). Deep structured learning for mass segmentation from mammograms. In Proceedings of the 2015 IEEE International Conference on Image Processing Vol. 2015-December (pp. 2950-2954). Quebec City, CANADA: IEEE.
DOI Scopus57 WoS45
2015 Chen, Q., & Carneiro, G. (2015). Artistic image analysis using the composition of human figures. In L. Agapito, M. Bronstein, & C. Rother (Eds.), Workshops Proceedings 13th European Conference on Computer Vision Vol. 1 (pp. 117-132). Switzerland: Springer International.
DOI Scopus1 WoS1
2015 Dhungel, N., Carneiro, G., & Bradley, A. (2015). Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms. In Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging Vol. 2015-July (pp. 760-763). New York, NY: IEEE.
DOI Scopus33 WoS22
2015 Dhungel, N., Carneiro, G., & Bradley, A. (2015). Deep learning and structured prediction for the segmentation of mass in mammograms. In N. Navab, J. Hornegger, W. Wells, & A. Frangi (Eds.), Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - Part 1, MICCAI 2015, Vol. 9349 (pp. 605-612). Munich, GERMANY: Springer.
DOI Scopus146 WoS115
2015 Carneiro, G., Nascimento, J., & Bradley, A. (2015). Unregistered multiview mammogram analysis with pre-trained deep learning models. In N. Navab, J. Hornegger, W. Wells, & A. Frangi (Eds.), Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part III - MICCAI 2015 Vol. 9351 (pp. 652-660). Munich, GERMANY: Springer.
DOI Scopus240 WoS178
2015 Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Flexible and latent structured output learning: Application to histology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9352 (pp. 220-228). Switzerland: Springer International Publishing.
DOI Scopus1
2015 Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Automatic detection of necrosis, normoxia and hypoxia in tumors from multimodal cytological images. In Proceedings of the 2015 IEEE International Conference on Image Processing Vol. 2015-December (pp. 2429-2433). Quebec City, CANADA: IEEE.
DOI Scopus4 WoS3
2015 Liao, Z., & Carneiro, G. (2015). The use of deep learning features in a hierarchical classifier learned with the minimization of a non-greedy loss function that delays gratification. In Proceedings of the 2015 IEEE International Conference on Image Processing Vol. 2015-December (pp. 4540-4544). Quebec City, CANADA: IEEE.
DOI Scopus3 WoS3
2015 Ngo, T., & Carneiro, G. (2015). Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference. In Proceedings - International Conference on Image Processing, ICIP Vol. 2015-December (pp. 2140-2143). Quebec City, CANADA: IEEE.
DOI Scopus34 WoS28
2015 Nascimento, J., & Carneiro, G. (2015). Towards reduction of the training and search running time complexities for non-rigid object segmentation. In Proceedings - International Conference on Image Processing, ICIP Vol. 2015-December (pp. 4713-4717). Quebec City, CANADA: IEEE.
DOI
2015 Johnston, A., Carneiro, G., Ding, R., & Velho, L. (2015). 3-D Modeling from Concept Sketches of Human Characters with Minimal User Interaction. In Proceedings of 2015 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). Adelaide, AUSTRALIA: IEEE.
DOI Scopus2 WoS67
2015 Dhungel, N., Carneiro, G., & Bradley, A. (2015). Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests. In Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (pp. 160-167). Adelaide, Australia: IEEE.
DOI Scopus213 WoS150
2015 Belagiannis, V., Rupprecht, C., Carneiro, G., & Navab, N. (2015). Robust optimization for deep regression. In Proceedings of the 2015 IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 2830-2838). Santiago, CHILE: IEEE.
DOI Scopus157 WoS133
2015 Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Weakly-supervised structured output learning with flexible and latent graphs using high-order loss functions. In Proceedings of the 2015 IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 648-656). Santiago, CHILE: IEEE.
DOI Scopus7 WoS6
2014 Nascimento, J., & Carneiro, G. (2014). Non-rigid segmentation using sparse low dimensional manifolds and deep belief networks. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 288-295). Columbus, OH: IEEE.
DOI Scopus8 WoS7
2014 Ngo, T., & Carneiro, G. (2014). Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3118-3125). Columbus, OH: IEEE.
DOI Scopus43 WoS35
2013 Ngo, T., & Carneiro, G. (2013). Left Ventricle Segmentation from Cardiac MRI Combining Level Set Methods with Deep Belief Networks.. In Proceedings of the IEEE 2013 20th International Conference on Image Processing (pp. 69-699). USA: IEEE.
DOI Scopus60 WoS57
2013 Nascimento, J., & Carneiro, G. (2013). Combining a bottom up and top down classifiers for the segmentation of the left ventricle from cardiac imagery. In Proceedings of the 2013 IEEE 20th International Conference on Image Processing, ICIP (pp. 743-746). USA: IEEE.
DOI
2013 Liu, W., Chin, T., Carneiro, G., & Suter, D. (2013). Point correspondence validation under unknown radial distortion. In Proceedings of the IEEE2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013 (pp. 1-8). USA: IEEE.
DOI Scopus2
2013 Carneiro, G., Liao, Z., & Chin, T. (2013). Closed-loop deep vision. In Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013 (pp. 1-8). USA: IEEE.
DOI
2013 Nascimento, J., & Carneiro, G. (2013). Top-down segmentation of non-rigid visual objects using derivative-based search on sparse manifolds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1963-1970). United States: IEEE.
DOI Scopus3 WoS3
2013 Dell'Agnello, D., Carneiro, G., Chin, T., Castellano, G., & Fanelli, A. (2013). Fuzzy clustering based encoding for visual object classification. In Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013 (pp. 1439-1444). USA: IEEE.
DOI Scopus4 WoS2
2013 Lu, Z., Carneiro, G., & Bradley, A. (2013). Automated nucleus and cytoplasm segmentation of overlapping cervical cells. In Proceedings of Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2013 Vol. 8149 LNCS (pp. 452-460). Germany: Springer.
DOI Scopus93 WoS70 Europe PMC14
2012 Nascimento, J., & Carneiro, G. (2012). On-line re-training and segmentation with reduction of the training set: application to the left ventricle detection in ultrasound imaging. In Proceedings of the 2012 19th IEEE International Conference on Image Processing, ICIP 2012 (pp. 2001-2004). USA: IEEE.
DOI Scopus1 WoS1
2012 Tran, Q., Chin, T., Carneiro, G., Brown, M., & Suter, D. (2012). In defence of RANSAC for outlier rejection in deformable registration. In Proceedings of the12th European Conference on Computer Vision, ECCV 2012 Vol. 7575 LNCS (pp. 274-287). Germany: Springer-Verlag.
DOI Scopus58 WoS51
2012 Carneiro, G., da Silva, N., Del Bue, A., & Costeira, J. (2012). Artistic image classification: an analysis on the PRINTART database. In Proceedings of the 12th European Conference on Computer Vision, ECCV 2012 Vol. 7575 LNCS (pp. 143-157). Germany: Springer-Verlag.
DOI Scopus67 WoS49
2012 Carneiro, G., & Nascimento, J. (2012). The use of on-line co-training to reduce the training set size in pattern recognition methods: application to left ventricle segmentation in ultrasound. In Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 948-955). USA: IEEE.
DOI Scopus9 WoS7
2011 Nascimento, J., & Carneiro, G. (2011). Reducing the training set using semi-supervised self-training algorithm for segmenting the left ventricle in ultrasound images. In Proceedings of 18th IEEE International Conference on Image Processing (ICIP 2011) (pp. 2021-2024). Belgium: IEEE.
DOI
2011 Carneiro, G., & Nascimento, J. (2011). Incremental on-line semi-supervised learning for segmenting the left ventricle of the heart from ultrasound data. In 2011 IEEE International Conference on Computer Vision (pp. 1700-1707). 345 E 47TH ST, NEW YORK, NY 10017 USA: IEEE.
DOI Scopus13 WoS11
2011 Carneiro, G. (2011). Graph-based methods for the automatic annotation and retrieval of art prints. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval (pp. 1-8). New York, USA: ACM.
DOI Scopus9
2011 Carneiro, G., Nascimento, J., & Freitas, A. (2011). Semi-supervised self-training model for the segmentation of the left ventricle of the heart from ultrasound data. In Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011 (pp. 1295-1301). Chicago, IL: IEEE Computer Society.
DOI
2011 da Silva, N., Marques, M., Carneiro, G., & Costeira, J. (2011). Explaining scene composition using kinematic chains of humans: application to Portuguese tiles history. In Proceedings of SPIE 2011 Volume 7869 Computer Vision and Image Analysis of Art II Vol. 7869 (pp. 1-9). California, USA: SPIE.
DOI Scopus3 WoS1
2011 Cabral, R., Costeira, J., De La Torre, F., Bernardino, A., & Carneiro, G. (2011). Time and order estimation of paintings based on visual features and expert priors. In Proceedings of SPIE - The International Society for Optical Engineering. Computer Vision and Image Analysis of Art II Vol. 7869 (pp. 1-10). Online: SPIE.
DOI Scopus5
2011 Carneiro, G., & Costeira, J. (2011). The automatic annotation and retrieval of digital images of prints and tile panels using network link analysis algorithms. In Proceedings of SPIE - The International Society for Optical Engineering. Computer Vision and Image Analysis of Art II Vol. 7869 (pp. 1-12). Online: SPIE.
DOI Scopus2
2011 Borcoci, E., Carneiro, G., & Iorga, R. (2011). Hybrid Multicast Management in a Content Aware Multidomain Network. In E. Borcoci, & J. Bi (Eds.), PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON ADVANCES IN FUTURE INTERNET (AFIN 2011) (pp. 90-95). FRANCE, Nice: IARIA XPS PRESS.
2010 Ricardo, M., Carneiro, G., Fortuna, P., Abrantes, F., & Dias, J. (2010). WiMetroNet - A scalable wireless network for metropolitan transports. In 6th Advanced International Conference on Telecommunications Aict 2010 (pp. 520-525). IEEE.
DOI Scopus7
2010 Carneiro, G. (2010). The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010 (pp. 3509-3516). USA: IEEE Computer Society.
DOI Scopus5 WoS3
2010 Carneiro, G., & Nascimento, J. (2010). Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010 (pp. 2815-2822). www: IEEE Computer Society.
DOI Scopus35 WoS24
2010 Nascimento, J., & Carneiro, G. (2010). Efficient search methods and deep belief networks with particle filtering for non-rigid tracking: application to lip tracking. In Proceedings of 17th IEEE International Conference on Image Processing (ICIP), 2010 (pp. 3817-3820). Online: IEEE Computer Society.
DOI Scopus1 WoS1
2010 Carneiro, G., & Nascimento, J. (2010). The fusion of deep learning architectures and particle filtering applied to lip tracking. In Proceedings of 20th International Conference on Pattern Recognition (ICPR), 2010 (pp. 2065-2068). USA: IEEE Computer society.
DOI Scopus7
2010 Carneiro, G. (2010). A comparative study on the use of an ensemble of feature extractors for the automatic design of local image descriptors. In Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010 (pp. 3356-3359). Online: IEEE computer society.
DOI
2010 Carneiro, G., Nascimento, J., & Freitas, A. (2010). Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods. In Proceedings of the 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1085-1088). NJ, USA: IEEE.
DOI Scopus35 WoS25
2009 Wels, M., Zheng, Y., Carneiro, G., Huber, M., Hornegger, J., & Comaniciu, D. (2009). Fast and robust 3-D MRI brain structure segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009:12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II Vol. 5762 LNCS (pp. 575-583). Heidelberg: Springer-Verlag Berlin.
DOI Scopus8 WoS17
2008 Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., & Comaniciu, D. (2008). A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. In MICCAI'08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I Vol. 5241 LNCS (pp. 1-8). Heidelberg: Springer-Verlag Berlin.
DOI Scopus24
2008 Carneiro, G., Amat, F., Georgescu, B., Good, S., & Comaniciu, D. (2008). Semantic-based indexing of fetal anatomies from 3-D ultrasound data using global/semi-local context and sequential sampling. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008 (pp. 1-8). Online: IEEE Computer Society.
DOI Scopus29 WoS1
2008 Wetterwald, M., Buburuzan, T., & Carneiro, G. (2008). Combining MBMS and IEEE 802.21 for on-the-road emergency. In Proceedings 2008 8th International Conference on Intelligent Transport System Telecommunications Itst 2008 (pp. 434-438). Phuket, THAILAND: IEEE.
DOI Scopus4 WoS4
2007 Sargento, S., Almeida, M., Corujo, D., Jesus, V., Aguiar, R. L., Godzecki, J., . . . Yáñez-Mingot. (2007). Integration of mobility and QoS in 4G scenarios. In Q2swinet 07 Proceedings of the Third ACM Workshop on Q2s and Security for Wireless and Mobile Networks (pp. 47-54). Chania, GREECE: ASSOC COMPUTING MACHINERY.
DOI Scopus12 WoS6
2007 Zhou, S., Guo, F., Park, J., Carneiro, G., Jackson, J., Simopoulos, C., . . . Comaniciu, D. (2007). A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure. In Proceedings of ICCV 2007 (pp. 1-8). USA: IEEE.
DOI Scopus20
2007 Zhou, S. K., Guo, F., Park, J. H., Carneiro, G., Jackson, J., Brendel, M., . . . Comaniciu, D. (2007). A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure. In 2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6 (pp. 1695-1702). BRAZIL, Rio de Janeiro: IEEE.
WoS7
2006 Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2006). Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree. In Proceedings of MICCAI 2007 Part 2 Vol. 4792 LNCS (pp. 571-579). Heidelberg: Springer-Verlag Berlin.
Scopus7 WoS13
2006 Vasconcelos, M., Carneiro, G., & Vasconcelos, N. (2006). Weakly supervised top-down image segmentation. In Proceedings of CVPR (1) 2006 Vol. 1 (pp. 1001-1006). USA: IEEE Computer Society.
DOI Scopus29
2006 Carneiro, G., & Lowe, D. (2006). Sparse flexible models of local features. In Proceedings of the 9th European Conference on Computer Vision (ECCV 2006), as published in Lecture Notes in Computer Science Vol. 3953 LNCS (pp. 29-43). Berlin, Heidelberg: Springer-Verlag.
DOI Scopus33 WoS17
2005 Carneiro, G., & Vasconcelos, N. (2005). Formulating semantic image annotation as a supervised learning problem. In Proceedings of CVPR 2005 Vol. II (pp. 163-168). USA: IEEE.
DOI Scopus126 WoS45
2005 Carneiro, G., & Jepson, A. (2005). The distinctiveness, detectability, and robustness of local image features. In Proceedings of CVPR 2005 Vol. II (pp. 296-301). USA: IEEE.
DOI Scopus14 WoS12
2005 Carneiro, G., & Vasconcelos, N. (2005). A database centric view of semantic image annotation and retrieval. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 559-566). New York, USA: ACM.
DOI Scopus48
2005 Carneiro, G., & Vasconcelos, N. (2005). Minimum Bayes error features for visual recognition by sequential feature selection and extraction. In Proceedings of the Second Canadian Conference on Computer and Robot Vision (CRV 05) (pp. 253-260). USA: IEEE.
DOI Scopus10 WoS3
2005 Fortuna, P., Carneiro, G., & Ricardo, M. (2005). Robust header compression in 4G networks with QoS support. In IEEE International Symposium on Personal Indoor and Mobile Radio Communications PIMRC Vol. 3 (pp. 1835-1839).
Scopus3
2004 Carneiro, G., & Jepson, A. D. (2004). Flexible spatial models for grouping local image features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2 (pp. II747-II754). DC, Washington: IEEE COMPUTER SOC.
Scopus28 WoS11
2004 Carneiro, G., & Jepson, A. D. (2004). Pruning local feature correspondences using shape context. In J. Kittler, M. Petrou, & M. Nixon (Eds.), Proceedings International Conference on Pattern Recognition Vol. 3 (pp. 16-19). British Machine Vis Assoc, Cambridge, ENGLAND: IEEE COMPUTER SOC.
DOI Scopus16 WoS7
2003 Carneiro, G., & Jepson, A. D. (2003). Multi-scale phase-based local features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 1 (pp. I/743). WI, MADISON: IEEE COMPUTER SOC.
Scopus91 WoS42
2002 Carneiro, G., & Jepson, A. D. (2002). Phase-based local features. In A. Heyden, G. Sparr, M. Nielsen, & P. Johansen (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 2350 (pp. 282-296). DENMARK, COPENHAGEN: SPRINGER-VERLAG BERLIN.
DOI Scopus59 WoS39
2002 Ricardo, M., Diaz, J., Carneiro, G., & Ruela, J. (2002). Support of IP QoS over UMTS networks. In IEEE International Symposium on Personal Indoor and Mobile Radio Communications PIMRC Vol. 4 (pp. 1909-1913). LISBON, PORTUGAL: IEEE.
DOI Scopus3
2002 Vasconcelos, N., & Carneiro, G. (2002). What is the role of independence for visual recognition?. In A. Heyden, G. Sparr, M. Nielsen, & P. Johansen (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 2350 (pp. 297-311). COPENHAGEN, DENMARK: SPRINGER-VERLAG BERLIN.
DOI Scopus7 WoS1
1999 Carneiro, G. H. M. B., Aude, E. P. L., Serdeira, H., Silveira, J. T. C., Martins, M. F., & Lopes, E. P. (1999). Internet request server architecture for telecommanding the CONTROLAB AGV through real time data and image. In J. RamirezAngulo (Ed.), Midwest Symposium on Circuits and Systems Vol. 2 (pp. 1074-1077). NM, NEW MEXICO STATE UNIV, CORBETT CTR, LAS CRUCES: IEEE.
Scopus1
1999 Aude, E. P. L., Silveira, J. T. C., Lopes, E. P., Carneiro, G. H. M. B., Serdeira, H., & Martins, M. F. (1999). Integration of intelligent systems and sensor fusion within the CONTROLAB AGV. In D. W. Gage, & H. M. Choset (Eds.), Proceedings of SPIE the International Society for Optical Engineering Vol. 3838 (pp. 50-62). BOSTON, MA: SPIE-INT SOC OPTICAL ENGINEERING.
DOI

Year Citation
2023 Tan, J. L., Pitawela, D., Chinnaratha, A., Chen, H. -T., Carneiro, G., & Singh, R. (2023). Enhancing accuracy in Barrett's surveillance using artificial intelligence: A multimodal (white-light and narrow-band imaging) model comparing vision transformer and convolutional neural networks. Poster session presented at the meeting of Abstracts of the Gastroenterological Society of Australia (GESA) Australian Gastroenterology Week (AGW) 2023, as published in Journal of gastroenterology and hepatology. Brisbane: Wiley.
DOI
2017 Carneiro, G., Oakden-Rayner, L., Bradley, A. P., Nascimento, J. C., & Palmer, L. J. (2017). Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography.. Poster session presented at the meeting of ISBI. IEEE.
2017 Carneiro, G., Tavares, J., Bradley, A., Papa, J., Nascimento, J., Cardoso, J., . . . Lu, Z. (2017). Preface DLMIA 2017. Poster session presented at the meeting of Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.
2017 Cheng, L. Z. T. P., Campbell, B., Carneiro, G., Burt, A. D., & Singh, R. (2017). Computer-aided diagnosis (CAD) for characterising colorectal lesions: Initial results of a newly developed software. Poster session presented at the meeting of JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY. WILEY.

Year Citation
2025 Bijlani, N., Gustavo., Nilforooshan, R., Group, C. T., Barnaghi, P., & Kouchaki, S. (2025). Remote Monitoring in Dementia Care - Lightweight, Explainable AI Validated for Early Warning of Health Events in the Home.
DOI
2024 Cordeiro, F., & Carneiro, G. (2024). Anne: Adaptive Nearest Neighbors and Eigenvector-Based Sample Selection for Robust Learning with Noisy Labels.
DOI
2024 Al-qershi, O., Nguyen, T., Elliott, M., Schmidt, D., Makalic, E., Li, S., . . . Hopper, J. (2024). AutoCumulus: an Automated Mammographic Density Measure Created Using Artificial Intelligence.
DOI
2024 Garg, A., Nguyen, C., Felix, R., Do, T. -T., & Carneiro, G. (2024). Pass: Peer-Agreement Based Sample Selection for Training with Instance-Dependent Noisy Labels.
DOI
2024 Hopper, J., Nguyen, T. L., Elliott, M. S., Al-qershi, O., Schmidt, D. F., Makalic, E., . . . Frazer, H. (2024). Braix Risk Score: An Automated Mammogram-Based Biomarker for Breast Cancer Created by Applying Artificial Intelligence.
DOI
2024 Masroor, M., Hassan, T., Tian, Y., Wells, K., Rosewarne, D., Do, T. -T., & Carneiro, G. (2024). Fair Distillation: Teaching Fairness from Biased Teachers in Medical
Imaging.
2024 Garg, A., Nguyen, C., Felix, R., Do, T. -T., & Carneiro, G. (2024). Pass: Peer-Agreement Based Sample Selection for Training with Instance Dependent Noisy Labels.
DOI
2024 Garg, A., Nguyen, C., Felix, R., Do, T. -T., & Carneiro, G. (2024). Pass: Peer-Agreement Based Sample Selection for Training with Instance Dependent Noisy Labels.
DOI
2023 Chen, Y., Liu, Y., Wang, H., Liu, F., Wang, C., Frazer, H., & Carneiro, G. (2023). Unraveling Instance Associations: A Closer Look for Audio-Visual
Segmentation.
2023 Galdran, A., Verjans, J., Carneiro, G., & Ballester, M. Á. G. (2023). Multi-Head Multi-Loss Model Calibration..
2023 Wang, C., Liu, Y., Chen, Y., Liu, F., Tian, Y., McCarthy, D. J., . . . Carneiro, G. (2023). Learning Support and Trivial Prototypes for Interpretable Image
Classification.
2023 Wang, C., Chen, Y., Liu, F., Liu, Y., McCarthy, D. J., Frazer, H., & Carneiro, G. (2023). Mixture of Gaussian-distributed Prototypes with Generative Modelling for
Interpretable and Trustworthy Image Recognition.
2023 Chen, Y., Liu, Y., Wang, C., Elliott, M., Kwok, C. F., Pena-Solorzano, C., . . . Carneiro, G. (2023). BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete
Annotations.
2023 Nguyen, C., Do, T. -T., & Carneiro, G. (2023). Task Weighting in Meta-learning with Trajectory Optimisation.
2022 Chen, Y., Liu, F., Wang, H., Wang, C., Tian, Y., Liu, Y., & Carneiro, G. (2022). BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray
Classification.
2022 Galdran, A., Carneiro, G., & Ballester, M. Á. G. (2022). On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness..
2022 Liu, Y., Tian, Y., Wang, C., Chen, Y., Liu, F., Belagiannis, V., & Carneiro, G. (2022). Translation Consistent Semi-supervised Segmentation for 3D Medical
Images.
2022 Liu, Y., Ding, C., Tian, Y., Pang, G., Belagiannis, V., Reid, I., & Carneiro, G. (2022). Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection
in Semantic Segmentation.
2022 Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2022). Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder..
2021 Tian, Y., Pu, L. Z. C. T., Liu, Y., Maicas, G., Verjans, J. W., Burt, A. D., . . . Carneiro, G. (2021). Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning..
2019 Felix, R., Sasdelli, M., Reid, I. D., & Carneiro, G. (2019). Multi-modal Ensemble Classification for Generalized Zero Shot Learning..

Centre of Excellence for Robotic Vision (ARC CoE 2014-2020)

Indo-Australian Biotechnology Fund (IABF) Project: New class of intelligent robotic imaging system for keyhole surgeries (2017-2020)

Discovery Project: Automated Analysis of Multi-modal Medical Data using Deep Belief Networks (ARC Discovery Project 2014-2016)

Linkage Infrastructure, Equipment and Facilities Project: Computational infrastructure for developing deep machine learning models (ARC LIEF 2016)

University of Adelaide - Interdisciplinary Research Fund Grant – Project Title: Novel Applications of Machine Learning in Healthcare (2016-2017). 

Automatic Quantification of Acute and Chronic Hypoxia in Tumors from Immunohistochemical Fluorescence Images using Deep Structured Inference (Humboldt Fellowship 2014-2015)

Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data (Portuguese Science Foundation FCT 2010-2012)

Printart: Where Computer Vision Meets Art (Portuguese Science Foundation FCT 2010-2012)

Learning to Combine Hierarchical Image Modeling with 2-D Segmentation and 3-D Pose Recovery of Visual Objects (Marie Curie International Incoming Fellowship 2010-2011)

University of Adelaide

​Instituto Superior Tecnico - University of Lisbon

University of Toronto

  • CSC 324 - Principles of Programming Languages (Fall 2004)
  • CSC 446, Computer Methods for Partial Differential Equations (TA) (Winter 2002). 
  • CSC 418, Computer Graphics (TA) (1999-2003).
  • CSC 458, Computer Networks (TA) (Winter 2000). 
  • CSC 258, Computer Organization (TA) (Summer 2000).
  • CSC 260, An Introduction to Scientific, Symbolic, and Graphical Computation (TA) (Winter 2003). 
  • SCI 199, Computer and Images. (TA) (2000-2001)

Date Role Research Topic Program Degree Type Student Load Student Name
2023 Co-Supervisor Artificial Intelligence and Endoscopy - Enhancing early detection and improving outcomes in dysplasia and cancer of the Upper Gastrointestinal Tracts. Doctor of Philosophy Doctorate Part Time Mr Jin Lin Tan
2023 Co-Supervisor Enhancing Medical Decision-Making with Machine Learning Master of Philosophy Master Full Time Mr Nilesh Ramgolam
2023 Co-Supervisor Enhancing Medical Decision-Making with Machine Learning Master of Philosophy Master Full Time Mr Nilesh Ramgolam
2023 Co-Supervisor Artificial Intelligence and Endoscopy - Enhancing early detection and improving outcomes in dysplasia and cancer of the Upper Gastrointestinal Tracts. Doctor of Philosophy Doctorate Full Time Mr Jin Lin Tan
2022 Co-Supervisor Improving Colorectal cancer detection through Colonoscopy exams with Explainable AI and Teachable AI Doctor of Philosophy Doctorate Full Time Mr Dileepa Pitawela
2022 Co-Supervisor Improving Colorectal cancer detection through Colonoscopy exams with Explainable AI and Teachable AI Doctor of Philosophy Doctorate Full Time Mr Dileepa Pitawela
2020 Co-Supervisor Computer Vision and Machine Learning for Navigation and Planning Doctor of Philosophy Doctorate Full Time Mr Sam Bahrami

Date Role Research Topic Program Degree Type Student Load Student Name
2021 - 2024 Principal Supervisor Exploiting Correspondences in Multi-perspective Data Learning Doctor of Philosophy Doctorate Full Time Mr Yuanhong Chen
2021 - 2024 Principal Supervisor Interpretable Deep Learning for Medical Imaging and Computer Vision Doctor of Philosophy Doctorate Full Time Mr Chong Wang
2021 - 2023 Principal Supervisor The Utility of Validation Sets for Meta-learning Methods for Noisy-Label and Imbalanced Learning Problems Master of Philosophy Master Full Time Mr Dung Anh Hoang
2021 - 2021 Principal Supervisor Relaxed Invariant Representation for Unsupervised Domain Adaptation Master of Philosophy Master Full Time Mr Hossein Askari Lyarjdameh
2021 - 2025 Principal Supervisor Instance-Aware Learning in the Presence of Label Noise: An Adaptive Framework for Image Classification Doctor of Philosophy Doctorate Full Time Mr Arpit Garg
2021 - 2025 Principal Supervisor Unpaired Multi-modal Multi-label Classification for Endometriosis Signs Doctor of Philosophy Doctorate Full Time Ms Yuan Zhang
2020 - 2024 Principal Supervisor Weakly-supervised learning in Computer Vision and Medical Imaging Doctor of Philosophy Doctorate Full Time Mr Fengbei Liu
2020 - 2024 Principal Supervisor Label Efficient Learning for Semantic Segmentation Doctor of Philosophy Doctorate Full Time Mr Yuyuan Liu
2019 - 2019 Principal Supervisor Efficient Deep Learning Models with Autoencoder Regularization and Information Bottleneck Compression Master of Philosophy Master Full Time Mr Jerome Oskar Williams
2019 - 2022 Principal Supervisor Anomaly Detection in Computer Vision and Medical Imaging Doctor of Philosophy Doctorate Full Time Mr Yu Tian
2018 - 2022 Principal Supervisor Harnessing meta-learning via probabilistic modelling and trajectory optimisation Doctor of Philosophy Doctorate Full Time Mr Cuong Cao Nguyen
2018 - 2022 Principal Supervisor Weakly Supervised Localisation for Censor Aware Survival Prediction from Medical Images Doctor of Philosophy Doctorate Full Time Mr Renato Hermoza Aragones
2017 - 2020 Co-Supervisor Endoscopy-Focused Primary, Secondary and Tertiary Prevention of Colorectal Cancer Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Dr Leonardo Zorron Cheng Tao Pu
2017 - 2020 Co-Supervisor Self-Supervised Learning for Geometry Doctor of Philosophy Doctorate Full Time Mr Huangying Zhan
2016 - 2022 Co-Supervisor Closing the implementation gap in pre-deployment medical AI study design Doctor of Philosophy Doctorate Part Time Dr Lauren Oakden-Rayner
2016 - 2020 Principal Supervisor Bayesian Data Augmentation and Generative Active Learning for Robust Imbalanced Deep Learning Doctor of Philosophy Doctorate Full Time Mr Toan Minh Tran
2016 - 2020 Principal Supervisor Data Augmentation for Multi-domain and Multi-model Generalised Zero-shot Learning Doctor of Philosophy Doctorate Full Time Dr Rafael Felix Alves
2015 - 2018 Principal Supervisor Pre-hoc and Post-hoc Diagnosis and Interpretation of Breast Magnetic Resonance Volumes Doctor of Philosophy Doctorate Full Time Mr Gabriel Maicas Suso
2015 - 2020 Principal Supervisor Single View 3D Reconstruction using Deep Learning Doctor of Philosophy Doctorate Part Time Adrian Robert Johnston
2013 - 2017 Principal Supervisor Methods for Understanding and Improving Deep Learning Classification Models Doctor of Philosophy Doctorate Full Time Dr Zhibin Liao
2013 - 2017 Co-Supervisor Moving Least Squares Registration in Computer Vision: New Applications and Algorithms Doctor of Philosophy Doctorate Full Time Mr Xiang Liu
2013 - 2016 Principal Supervisor Automated Detection, Segmentation and Classification of Masses from Mammograms using Deep Learning Doctor of Philosophy Doctorate Full Time Mr Neeraj Dhungel
2011 - 2016 Principal Supervisor Medical Image Segmentation Combining Level Set Method and Deep Belief Networks Doctor of Philosophy Doctorate Full Time Mr Tuan Anh Ngo

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