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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
12972016
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
11882007
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
3182016
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
3022018
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
2912017
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
2862017
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
2842018
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
2802015
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
2672017
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
2292015
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
2292008
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
2142011
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
Multi-scale phase-based local features
G Carneiro, AD Jepson
2003 IEEE Computer Society Conference on Computer Vision and Pattern …, 2003
2002003
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
1932015
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
1912017
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
1892020
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
1882015
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
1572015
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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