| Supervised learning of semantic classes for image annotation and retrieval G Carneiro, AB Chan, PJ Moreno, N Vasconcelos IEEE transactions on pattern analysis and machine intelligence 29 (3), 394-410, 2007 | 1090 | 2007 |
| Unsupervised cnn for single view depth estimation: Geometry to the rescue R Garg, VK Bg, G Carneiro, I Reid European conference on computer vision, 740-756, 2016 | 633 | 2016 |
| Formulating semantic image annotation as a supervised learning problem G Carneiro, N Vasconcelos 2005 IEEE Computer Society Conference on Computer Vision and Pattern …, 2005 | 203 | 2005 |
| Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions V Kumar BG, G Carneiro, I Reid Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 196 | 2016 |
| Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree G Carneiro, B Georgescu, S Good, D Comaniciu IEEE transactions on medical imaging 27 (9), 1342-1355, 2008 | 192 | 2008 |
| Multi-scale phase-based local features G Carneiro, AD Jepson 2003 IEEE Computer Society Conference on Computer Vision and Pattern …, 2003 | 183 | 2003 |
| Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance TA Ngo, Z Lu, G Carneiro Medical image analysis 35, 159-171, 2017 | 175 | 2017 |
| Unregistered multiview mammogram analysis with pre-trained deep learning models G Carneiro, J Nascimento, AP Bradley International Conference on Medical Image Computing and Computer-Assisted …, 2015 | 164 | 2015 |
| Automated mass detection in mammograms using cascaded deep learning and random forests N Dhungel, G Carneiro, AP Bradley 2015 international conference on digital image computing: techniques and …, 2015 | 139 | 2015 |
| The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods G Carneiro, JC Nascimento, A Freitas IEEE Transactions on Image Processing 21 (3), 968-982, 2011 | 136 | 2011 |
| Phase-based local features G Carneiro, AD Jepson European Conference on Computer Vision, 282-296, 2002 | 128 | 2002 |
| Robust optimization for deep regression V Belagiannis, C Rupprecht, G Carneiro, N Navab Proceedings of the IEEE international conference on computer vision, 2830-2838, 2015 | 124 | 2015 |
| An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells Z Lu, G Carneiro, AP Bradley IEEE Transactions on Image Processing 24 (4), 1261-1272, 2015 | 117 | 2015 |
| A deep learning approach for the analysis of masses in mammograms with minimal user intervention N Dhungel, G Carneiro, AP Bradley Medical image analysis 37, 114-128, 2017 | 116 | 2017 |
| Sparse flexible models of local features G Carneiro, D Lowe European Conference on Computer Vision, 29-43, 2006 | 94 | 2006 |
| Smart mining for deep metric learning B Harwood, V Kumar BG, G Carneiro, I Reid, T Drummond Proceedings of the IEEE International Conference on Computer Vision, 2821-2829, 2017 | 93 | 2017 |
| Automated nucleus and cytoplasm segmentation of overlapping cervical cells Z Lu, G Carneiro, AP Bradley International Conference on Medical Image Computing and Computer-Assisted …, 2013 | 92 | 2013 |
| Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data G Carneiro, JC Nascimento IEEE transactions on pattern analysis and machine intelligence 35 (11), 2592 …, 2013 | 89 | 2013 |
| Deep learning and structured prediction for the segmentation of mass in mammograms N Dhungel, G Carneiro, AP Bradley International Conference on Medical image computing and computer-assisted …, 2015 | 86 | 2015 |
| A bayesian data augmentation approach for learning deep models T Tran, T Pham, G Carneiro, L Palmer, I Reid Advances in neural information processing systems, 2797-2806, 2017 | 84 | 2017 |
Andrew P. BradleyQueensland University of TechnologyVerified email at ieee.org
