Publications

2017

1. Gustavo Carneiro, Jacinto C. Nascimento and Andrew Bradley. Automated Analysis of Unregistered Multi-view Mammograms with Deep Learning. Paper accepted by IEEE Transactions on Medical Imaging (TMI) in 2017

2. Gustavo Carneiro, Tingying Peng, Christine Bayer, and Nassir Navab. Automatic Quantification of Tumour Hypoxia from Multi-modal Microscopy Images using Weakly-Supervised Learning Methods. Paper accepted for publication by IEEE Transactions on Medical Imaging (TMI). 2017.

3. Tuan Anh Ngo, Zhi Lu and Gustavo Carneiro. 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 (2017): 159-171.

4. Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley. A Deep Learning Approach for the Analysis of Masses in Mammograms with Minimal User Intervention. Paper accepted for publication on 25-01-2017 by Medical Image Analysis. 2017.

5. Le Lu, Y. Zheng, G. Carneiro, L. Yang (Editors). Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer 2017 (ISBN 978-3-319-42999-1).

6. Cardoso, J., Arbel, T., Carneiro, G., Syeda-Mahmood, T., Tavares, J.M.R.S., Moradi, M., Bradley, A., Greenspan, H., Papa, J.P., Madabushi, A., Nascimento, J.C., Cardoso, J.S., Belagiannis, V., Lu, Z. eds. (2017) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings. Held in conjunction with MICCAI 2017, Quebec City, Canada, OSeptmeber 19, 2017.

7. Tuan Anh Ngo and Gustavo Carneiro. Fully automated Segmentation using Distance Regularized Level Set and Deep-structured Learning and Inference. Book chapter in Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer 2017 (ISBN 978-3-319-42999-1).

8. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms. Book chapter in Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer 2017 (ISBN 978-3-319-42999-1).

9. Gustavo Carneiro, Jacinto Nascimento and Andrew Bradley. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-view Images and Segmentation Maps of Lesions. Book chapter in Deep Learning for Medical Image Analysis. Elsevier/Academic Press 2017 (ISBN 9780128104088).

10. Gabriel Maicas, Gustavo Carneiro, Jacinto Nascimento, Andrew Bradley, Ian Reid. Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017)

11. Gustavo Carneiro, Luke Oakden-Rayner, Andrew Peter Bradley, Jacinto Nascimento, Lyle Palmer (2017). Automated 5-Year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography. International Symposium on Biomedical Imaging (ISBI) 2017.

12. Gabriel Maicas Suso, Gustavo Carneiro, Andrew Peter Bradley (2017). Globally Optimal Breast Mass Segmentation from DCE-MRI Using Deep Semantic Segmentation As Shape Prior. International Symposium on Biomedical Imaging (ISBI) 2017.

13. Neeraj Dhungel, Gustavo Carneiro, Andrew Peter Bradley (2017). Fully Automated Classification of Mammograms Using Deep Residual Neural Networks. International Symposium on Biomedical Imaging (ISBI) 2017.

14. Hang Min, Shekhar Chandra, Neeraj Dhungel, Stuart Crozier, Andrew P. Bradley (2017). Multi-scale Mass Segmentation for Mammograms via Cascaded Random Forests. International Symposium on Biomedical Imaging (ISBI) 2017.

2016

1. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. The Automated Learning of Deep Features for Breast Mass Classification from Mammograms. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016)

2. Gustavo Carneiro, Diana Mateus, Peter Loïc, Andrew Bradley, João Manuel RS Tavares, Vasileios Belagiannis, João Paulo Papa et al., eds. (2016) Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. Vol. 10008. Springer, 2016.

3. Z. Lu, G. Carneiro, A. Bradley, D. Ushizima, M. S. Nosrati, A. Bianchi, C. Carneiro, and G. Hamarneh. Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells. IEEE journal of biomedical and health informatics (2016)

4. Jacinto Nascimento and Gustavo Carneiro. Tracking and Segmentation of the Endocardium of the Left Ventricle in 2D Ultrasound using Deep Learning Architectures and Monte Carlo Sampling. Book Chapter in Biomedical Image Segmentation: Advances and Trends. Editors: Ayman El-Baz, Xiaoyi Jiang, Jasjit S. Suri. CRC Press, ISBN 9781482258554, 2016.

5. Jacinto Nascimento and Gustavo Carneiro. Multi-Atlas Segmentation Using Manifold Learning with Deep Belief Networks. IEEE International Symposium on Biomedical Imaging (ISBI) 2016

2015

1. Gustavo Carneiro, Tingying Peng, Christine Bayer and Nassir Navab. Weakly-supervised Structured Output Learning with Flexible and Latent Graphs using High-order Loss Functions. In Proceedings of the International Conference on Computer Vision (ICCV) 2015.

2. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. Automated Mass Detection from Mammograms using Cascaded Deep Learning and Random Forests. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2015.

3. Gustavo Carneiro, Jacinto Nascimento and Andrew Bradley. Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models. In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2015.

4. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms. In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2015.

5. Gustavo Carneiro, Tingying Peng, Christine Bayer and Nassir Navab. Flexible and Latent Structured Output Learning: Application to Histology. In MICCAI Workshop Machine Learning in Medical Imaging (MLMI) 2015.

6. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. Automated Mass Detection from Mammograms using Deep Learning and Random Forest. In MICCAI Workshop Breast Image Analysis (BIA) 2015.

7. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. Deep Structured Learning for Mass Segmentation from Mammograms. In Proceddings of the International Conference on Image Processing (ICIP) 2015.

8. Tuan Anh Ngo and Gustavo Carneiro. Lung Segmentation in Chest Radiographs using Distance Regularized Level set and Deep-Structure Learning and Inference. In Proceddings of the International Conference on Image Processing (ICIP) 2015.

9. Jacinto Nascimento and Gustavo Carneiro. Towards Reduction of the Training and Search Running Time Complexities for Non-Rigid Object Segmentation. In Proceddings of the International Conference on Image Processing (ICIP) 2015.

10. Zhi Lu, Gustavo Carneiro and Andrew Bradley. An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells. IEEE Transactions on Image Processing. Vol. 24, No. 4, 2015.

11. Neeraj Dhungel, Gustavo Carneiro and Andrew Bradley. Tree Re-Weighted Belief Propagation Using Deep Learning Potentials for Mass Segmentation from Mammograms. In Proceddings of the International Symposium on Biomedical Imaging (ISBI) 2015.

2014

1. Tuan Anh Ngo and Gustavo Carneiro. Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Cosntrained by Deep-structured Inference. CVPR 2014.

2. Jacinto Nascimento and Gustavo Carneiro. Non-rigid Segmentation using Sparse Low dimensional Manifolds and Deep Belief Networks. CVPR 2014.