
Professor Gustavo Carneiro
Professor
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences
Eligible to supervise Masters and PhD, but is currently at capacity - 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.
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Appointments
Date Position Institution name 2015 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 Competencies
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 -
Education
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 -
Research Interests
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Journals
Year Citation 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, 107585-1-107585-12.
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.. CoRR, abs/2101.03285. 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), 11 pages.
2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features.. CoRR, abs/2101.10030. 2021 Bedrikovetski, S., Dudi-Venkata, N. N., Maicas, 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, 102022.
2021 Ang, T. L., & Carneiro, G. (2021). Artificial intelligence in gastrointestinal endoscopy. Journal of Gastroenterology and Hepatology (Australia), 36(1), 5-6.
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.
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.
Scopus3 WoS4 Europe PMC12020 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.
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., 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.
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 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.
Scopus4 WoS1 Europe PMC12020 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 open, 10(8), e035446.
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.
Scopus6 WoS52020 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.
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.
Scopus2 WoS22020 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.
Scopus12020 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.
Scopus12020 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.
Scopus3 WoS22020 Antico, M., Sasazawa, F., Dunnhofer, M., Camps, S., Jaiprakash, A., Pandey, A., . . . 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.
Scopus5 WoS62020 Camps, S., 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.
Scopus12020 Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Re, C. (2020). Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. ACM CHIL 2020 - Proceedings of the 2020 ACM Conference on Health, Inference, and Learning, 2020, 151-159.
Scopus9 Europe PMC42019 Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised zero-shot learning with a classifier ensemble over multi-modal embedding spaces. arXiv, OnlinePubl. 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.
Scopus5 WoS52019 Felix, R., Sasdelli, M., Reid, I. D., & Carneiro, G. (2019). Multi-modal Ensemble Classification for Generalized Zero Shot Learning.. CoRR, abs/1901.04623. 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.
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.
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.
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.
Scopus121 WoS98 Europe PMC222017 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.
Scopus53 WoS44 Europe PMC42017 Nascimento, J., & Carneiro, G. (2017). Deep learning on sparse manifolds for faster object segmentation. IEEE Transactions on Image Processing, 26(10), 4978-4990.
Scopus7 WoS10 Europe PMC22017 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.
Scopus153 WoS136 Europe PMC362017 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.
Scopus43 WoS32 Europe PMC52017 Ribeiro, D., Nascimento, J., Bernardino, A., & Carneiro, G. (2017). Improving the performance of pedestrian detectors using convolutional learning. Pattern Recognition, 61, 641-649.
Scopus24 WoS222017 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.
Scopus60 WoS57 Europe PMC252017 Liao, Z., & Carneiro, G. (2017). A deep convolutional neural network module that promotes competition of multiple-size filters. Pattern Recognition, 71, 94-105.
Scopus14 WoS152017 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.
Scopus2 WoS2 Europe PMC12015 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.
Scopus111 WoS87 Europe PMC162015 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. 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.
Scopus1 WoS12013 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.
Scopus76 WoS69 Europe PMC142013 Carneiro, G. (2013). Artistic image analysis using graph-based learning approaches. IEEE Transactions on Image Processing, 22(8), 3168-3178.
Scopus4 WoS42012 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.
Scopus120 WoS104 Europe PMC222012 Carneiro, G., Fortuna, P., Dias, J., & Ricardo, M. (2012). Transparent and scalable terminal mobility for vehicular networks. Computer Networks, 56(2), 577-597.
Scopus2 WoS12012 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.
Scopus1 WoS12011 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.
Scopus18 WoS112009 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.
Scopus15 Europe PMC22009 Carneiro, G., & Vasconcelos, N. (2009). Minimum Bayes error features for visual recognition. Image and Vision Computing, 27(1-2), 131-140.
Scopus2 WoS22009 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.
Scopus9 WoS72009 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.
Scopus12 WoS13 Europe PMC42008 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.
Scopus131 WoS116 Europe PMC302008 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.
Scopus57 WoS37 Europe PMC42007 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.
Scopus16 Europe PMC22007 Carneiro, G., & Ricardo, M. (2007). QoS abstraction layer in 4G access networks. Telecommunication Systems, 35(1-2), 55-65.
Scopus2 WoS12007 Carneiro, G., & Jepson, A. (2007). Flexible spatial configuration of local image features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2089-2104.
Scopus49 WoS33 Europe PMC52007 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.
Scopus743 WoS485 Europe PMC342004 Carneiro, G., Ruela, J., & Ricardo, M. (2004). Cross-layer design in 4G wireless terminals. IEEE Wireless Communications, 11(2), 7-13.
Scopus126 WoS731999 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.
Scopus12 WoS11— Lu, Z., Carneiro, G., Dhungel, N., & Bradley, A. P. (n.d.). Automated Detection of Individual Micro-calcifications from Mammograms
using a Multi-stage Cascade Approach.— Nguyen, C., Do, T. -T., & Carneiro, G. (n.d.). Similarity of Classification Tasks. — Hermoza, R., Maicas, G., Nascimento, J. C., & Carneiro, G. (n.d.). Post-hoc Overall Survival Time Prediction from Brain MRI. — Tian, Y., Pang, G., Liu, F., chen, Y., Shin, S. H., Verjans, J. W., . . . Carneiro, G. (n.d.). Constrained Contrastive Distribution Learning for Unsupervised Anomaly
Detection and Localisation in Medical Images. -
Books
Year Citation 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.
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.
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Book Chapters
Year Citation 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.
Scopus12019 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.
Scopus12019 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.
Scopus12019 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., Bradley, A., Papa, J., Belagiannis, V., Nascimento, J., . . . 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., Bradley, A., Papa, J., Belagiannis, V., Nascimento, J., . . . 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.
Scopus112017 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.
Scopus152017 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.
Scopus32017 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.
Scopus52016 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.
Scopus22015 Chen, Q., & Carneiro, G. (2015). Artistic Image Analysis Using the Composition of Human Figures. In Computer Vision - ECCV 2014 Workshops (pp. 117-132). Springer International Publishing.
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Conference Papers
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Conference Items
Year Citation 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. -
Patents
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
- Puzzle-Based Learning (Fall 2017)
- Computer Graphics (Fall 2017)
- Topics in Computer Science (Fall 2017)
- Advanced Topics in Computer Science (Fall 2017)
- Puzzle-Based Learning (Fall 2016)
- Topics in Computer Science (Fall 2016)
- Object Oriented Programming (Spring 2015)
- Topics in Computer Science (Spring 2015)
- Computer Graphics (Fall 2015) - Videos of Best Projects
- Puzzle-Based Learning (Fall 2015)
- Topics in Computer Science (Fall 2015)
- Puzzle-Based Learning (Fall 2014)
- Software Engineering in Industry (Fall 2014)
- Topics in Computer Science (Fall 2014)
- Puzzle-Based Learning (Spring 2013)
- Software Engineering Group Project 1B (Spring 2013)
- Master of Software Engineering Project (Spring 2013)
- Computer Graphics (Fall 2013) - Videos of Best Projects
- Puzzle-Based Learning (Fall 2013)
- Puzzle-Based Learning (Spring 2012)
- Computer Vision (Fall 2012)
- Computer Graphics (Fall 2012) - Videos of Best Projects
Instituto Superior Tecnico - University of Lisbon
- Signals and Systems (Fall 2009)
- Robotics (Spring 2009)
- Modeling and Simulation (Spring 2009)
- Signal Processing (Fall 2008)
- Control (Fall 2008)
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)
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2021 Principal Supervisor Medical Image Analysis, Machine Learning, Deep Learning, Computer Vision, Image Processing Doctor of Philosophy Doctorate Full Time Mr Chong Wang 2021 Principal Supervisor Unsupervised Anomaly Detection and Localisation in Real-world Images Doctor of Philosophy Doctorate Full Time Mr Yuanhong Chen 2021 Principal Supervisor Active Learning for Noisy Label Master of Philosophy Master Full Time Mr Dung Anh Hoang 2020 Co-Supervisor Computer Vision and Machine Learning for Navigation and Planning Doctor of Philosophy Doctorate Full Time Mr Sam Bahrami 2020 Principal Supervisor Adapting deep learning for real-world image datasets Doctor of Philosophy Doctorate Full Time Mr Fengbei Liu 2020 Principal Supervisor large-scale interval cancer detection in mammograms with an interpretable and explainable model Doctor of Philosophy Doctorate Full Time Michael Llewellyn Mogford 2020 Principal Supervisor Domain adaptation and generalization methods for noisy-label learning Doctor of Philosophy Doctorate Full Time Mr Yuyuan Liu 2019 Co-Supervisor Using Machine Learning to Predict Indicators of Glaucoma Progression Doctor of Philosophy Doctorate Full Time Mr Ryan Pham 2019 Principal Supervisor Investigating Cancer Genomic Evolution and Personalised Therapies with Machine Learning Doctor of Philosophy Doctorate Part Time Mr Po Liu 2019 Principal Supervisor Medical Machine Learning Doctor of Philosophy Doctorate Full Time Mr Yu Tian 2019 Principal Supervisor Improving Dataset Effectiveness by Reducing Task Irrelevant Variation Doctor of Philosophy Doctorate Full Time Mr Gerard Snaauw 2018 Principal Supervisor Computer Vision and Machine Learning Doctor of Philosophy Doctorate Full Time Mr Cuong Cao Nguyen 2018 Co-Supervisor Deep Learning for Screening Mammography Master of Clinical Science Master Part Time Dr James John Joseph Condon 2018 Principal Supervisor Discovering and Localizing Biomarkers Associated with Chronic and Age-Related Diseases for Computed Tomographic Scans Doctor of Philosophy Doctorate Full Time Mr Renato Hermoza Aragones 2018 Principal Supervisor Deep Transfer Learning Across Domains and Tasks for Medical Images Doctor of Philosophy Doctorate Full Time Mr Hossein Askari Lyarjdameh 2016 Co-Supervisor Radiomics and Deep learning methods to identify chronic disease in medical images Doctor of Philosophy Doctorate Part Time Mr Luke Oakden-Rayner -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 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 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 - 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 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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