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Gustavo Carneiro
Gustavo Carneiro
Verified email at adelaide.edu.au - Homepage
Title
Cited by
Cited by
Year
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
12622016
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
11862007
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
3092016
Multi-modal cycle-consistent generalized zero-shot learning
R Felix, I Reid, G Carneiro
Proceedings of the European Conference on Computer Vision (ECCV), 21-37, 2018
2902018
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
2852017
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
2782017
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
2762015
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS …
D Stoyanov, Z Taylor, G Carneiro, T Syeda-Mahmood, A Martel, ...
Springer, 2018
2652018
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
2622017
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
2272008
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
2252015
Formulating semantic image annotation as a supervised learning problem
G Carneiro, N Vasconcelos
2005 IEEE Computer Society Conference on Computer Vision and Pattern …, 2005
2112005
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
2102011
Multi-scale phase-based local features
G Carneiro, AD Jepson
2003 IEEE Computer Society Conference on Computer Vision and Pattern …, 2003
2012003
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
1912015
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 30, 2017
1872017
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
1842015
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L Oakden-Rayner, J Dunnmon, G Carneiro, C Ré
Proceedings of the ACM conference on health, inference, and learning, 151-159, 2020
1732020
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
1532015
Deep learning and convolutional neural networks for medical image computing
L Lu, Y Zheng, G Carneiro, L Yang
Advances in computer vision and pattern recognition 10, 978-3, 2017
1472017
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