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Associate Professor Gustavo Carneiro

Gustavo Carneiro
Associate Professor
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences

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Associate Professor Gustavo Carneiro

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

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

Journals

Year Citation
2018 Carneiro, G., Tavares, J., Bradley, A., Papa, J., Nascimento, J., Cardoso, J., . . . 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
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 Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2018). Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
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 Scopus32 Europe PMC8
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 Scopus5 WoS3
2017 Ngo, T., 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 Scopus46 WoS33 Europe PMC14
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 Scopus10 WoS8
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 Scopus11 WoS7 Europe PMC2
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 Scopus20 WoS18 Europe PMC12
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 Scopus3 WoS2
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 Scopus2 WoS2 Europe PMC1
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 Scopus1 WoS1
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 Scopus50 WoS40 Europe PMC12
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 Scopus42 WoS37 Europe PMC8
2013 Carneiro, G. (2013). Artistic image analysis using graph-based learning approaches. IEEE Transactions on Image Processing, 22(8), 3168-3178.
DOI Scopus4 WoS4
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 Scopus61 WoS46 Europe PMC15
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 Scopus7 WoS6
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 Scopus5 WoS5 Europe PMC3
2009 Wels, M., Zheng, Y., Carneiro, G., Huber, M., Hornegger, J., & Comaniciu, D. (2009). Fast and robust 3-D MRI brain structure segmentation.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12(Pt 2), 575-583.
Scopus5
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.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11(Pt 1), 67-75.
Scopus25 WoS27 Europe PMC3
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 Scopus98 WoS91 Europe PMC24
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 Scopus47 WoS33 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 Scopus641 WoS398 Europe PMC33
2007 Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2007). Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 10(Pt 2), 571-579.
Scopus8 Europe PMC1
2007 Carneiro, G., & Ricardo, M. (2007). QoS abstraction layer in 4G access networks. Telecommunication Systems, 35(1-2), 55-65.
DOI Scopus2 WoS1
1999 Aude, E., Carneiro, G., Serdeira, H., Silveira, J., Martins, M., & Lopes, E. (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.
Snaauw, G., Gong, D., Maicas, G., Hengel, A. V. D., Niessen, W. J., Verjans, J., & Carneiro, G. (n.d.). End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic
Resonance Imaging.

Books

Year Citation
2016 Carneiro, G., Tavares, J., Bradley, A., Papa, J., Nascimento, J., Cardoso, J., . . . 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).

Book Chapters

Year Citation
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 Scopus4
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
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 Scopus2
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 Scopus1
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 Biomedical Image Segmentation: Advances and Trends (pp. 387-406).
DOI

Conference Papers

Year Citation
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). Piscataway, N.J.: IEEE.
DOI
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.
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) (pp. 1-10). Long Beach, CA, USA.
2018 Maicas, G., Bradley, A., Nascimento, J., Reid, I., & Carneiro, G. (2018). Training medical image analysis systems like radiologists. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11070 LNCS (pp. 546-554).
DOI
2018 Felix, R., Vijay Kumar, B., Reid, I., & Carneiro, G. (2018). Multi-modal cycle-consistent generalized zero-shot learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11210 LNCS (pp. 21-37).
DOI
2018 Pham, T., Vijay Kumar, B., Do, T., Carneiro, G., & Reid, I. (2018). Bayesian semantic instance segmentation in open set world. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11214 LNCS (pp. 3-18).
DOI
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 Tran, T., Pham, T., Carneiro, G., Palmer, L., & Reid, I. (2017). A Bayesian data augmentation approach for learning deep models. In Advances in Neural Information Processing Systems Vol. 2017-December (pp. 2798-2807).
Scopus4
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 Scopus2
2017 Pu, L., Campbell, B., Carneiro, G., Burt, A., & 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 Maicas, G., Carneiro, G., Bradley, A., Nascimento, J., & Reid, I. (2017). Deep reinforcement learning for active breast lesion detection from DCE-MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10435 LNCS (pp. 665-673).
DOI Scopus3
2017 Ribeiro, D., Carneiro, G., Nascimento, J., & Bernardino, A. (2017). Multi-channel convolutional neural network ensemble for pedestrian detection. In L. Alexandre, J. Sanchez, & J. Rodrigues (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10255 LNCS (pp. 122-130). Univ Algarve, Faro, PORTUGAL: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI
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). Piscataway, NJ: IEEE.
DOI Scopus1 WoS1
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). Piscataway, NJ: IEEE.
DOI Scopus12 WoS5
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). Piscataway, NJ: IEEE.
DOI Scopus2 WoS1
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 Scopus6
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 Scopus17
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 Scopus1 WoS1
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-January (pp. 5385-5394). Las Vegas, NV: IEEE.
DOI Scopus41 WoS12
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 Scopus57 WoS14
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 Scopus1
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 Scopus7
2015 Chen, Q., & Carneiro, G. (2015). Artistic image analysis using the composition of human figures. In L. Agapito, M. M. Bronstein, & C. Rother (Eds.), Workshops Proceedings 13th European Conference on Computer Vision Vol. 1 (pp. 117-132). Switzerland: Springer International.
DOI
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 Scopus6 WoS3
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 Scopus30 WoS14
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 Scopus49 WoS17
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).
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 Scopus3 WoS2
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 Scopus11 WoS7
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 Scopus12 WoS7
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 Scopus1 WoS3
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 Scopus37
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 Scopus30 WoS16
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 Scopus3 WoS2
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 Scopus3 WoS3
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 Scopus22 WoS15
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 Scopus22 WoS19
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. J., 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 Scopus1
2013 Carneiro, G., Liao, Z., & Chin, T. J. (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 Scopus2 WoS1
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 Scopus2
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 Scopus25 WoS27 Europe PMC1
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
2012 Tran, Q., Chin, T. J., 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 Scopus27 WoS16
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 Scopus12 WoS5
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 Scopus4 WoS3
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 Scopus4 WoS3
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 Scopus8
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
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 Scopus4
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
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).
DOI Scopus5
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 Scopus23 WoS9
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
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 Scopus4
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 Scopus21 WoS16
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 Scopus7 WoS8
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 Scopus23
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 Scopus25
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 Scopus9
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.
Scopus5 WoS6
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 Scopus26
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 Scopus24 WoS11
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 Scopus111 WoS21
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 Scopus13 WoS5
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 Scopus45
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 Scopus7 WoS2
2004 Carneiro, G., & Jepson, A. (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 Scopus15 WoS1
2004 Carneiro, G., & Jepson, A. (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.
Scopus24
2003 Carneiro, G., & Jepson, A. (2003). Multi-scale phase-based local features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 1.
Scopus81
2002 Carneiro, G., & Jepson, A. (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). COPENHAGEN, DENMARK: SPRINGER-VERLAG BERLIN.
Scopus51 WoS19
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.
Scopus6 WoS1
1999 Aude, E., Silveira, J., Lopes, E., Carneiro, G., Serdeira, H., & Martins, M. (1999). Integration of intelligent systems and sensor fusion within the CONTROLAB AGV. In D. Gage, & H. Choset (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 3838 (pp. 50-62). BOSTON, MA: SPIE-INT SOC OPTICAL ENGINEERING.
DOI
1999 Carneiro, G., Aude, E., Serdeira, H., Silveira, J., Martins, M., & Lopes, E. (1999). Internet request server architecture for telecommanding the CONTROLAB AGV through real time data and image. In Midwest Symposium on Circuits and Systems Vol. 2 (pp. 1074-1077).
Scopus1

Conference Items

Year Citation
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.
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.

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

​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)

Current Higher Degree by Research Supervision (University of Adelaide)

Date Role Research Topic Program Degree Type Student Load Student Name
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 Localizing Organs Responsible for Mortality Prediction on Cross-Sectional Clinical Images Doctor of Philosophy Doctorate Full Time Mr Renato Hermoza Aragones
2017 Co-Supervisor Feature learning in deep networks for robotic vision Doctor of Philosophy Doctorate Full Time Mr Huangying Zhan
2017 Co-Supervisor Image Enhance Endoscopy in Sessile Serrated Lesions in the Colon Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Dr Leonardo Zorron Cheng Tao Pu
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
2016 Principal Supervisor Computer Vision and Machine Learning and Medical Image Analysis Doctor of Philosophy Doctorate Full Time Mr Toan Minh Tran
2016 Principal Supervisor Computer Vision and Machine Learning Doctor of Philosophy Doctorate Full Time Mr Rafael Felix Alves
2015 Principal Supervisor Deep Learning for Detection, Segmentation and Classification of Masses of Microcalcifictions in MRI for Estimating Breast Cancer Probability Doctor of Philosophy Doctorate Full Time Mr Gabriel Maicas
2015 Principal Supervisor Large Scale Geospatial Image Understanding and Visualisation Doctor of Philosophy Doctorate Full Time Adrian Robert Johnston
2015 Principal Supervisor Improving Safety through Computer Vision Doctor of Philosophy Doctorate Full Time Mr Jerome Oskar Williams

Past Higher Degree by Research Supervision (University of Adelaide)

Date Role Research Topic Program Degree Type Student Load Student Name
2013 - 2017 Principal Supervisor Methods for Understanding and Improving Deep Learning Classification Models Doctor of Philosophy Doctorate Full Time Mr 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
Position
Associate Professor
Phone
83136164
Fax
8313 4366
Campus
North Terrace
Building
Ingkarni Wardli, floor 5
Room Number
5 42
Org Unit
School of Computer Science

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