1.3k citations · 3.6k across the 13 of their papers we have counts for
14 papers
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
Malo de Boisredon, Eugene Vorontsov, William Trung Le +1
Deep neural networks are commonly used for automated medical image segmentation, but models will frequently struggle to generalize well across different imaging modalities. This is…
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision
Malo Alefsen de Boisredon d'Assier, Eugene Vorontsov, Samuel Kadoury
Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different i…
Label noise in segmentation networks : mitigation must deal with bias
Eugene Vorontsov, Samuel Kadoury
Imperfect labels limit the quality of predictions learned by deep neural networks. This is particularly relevant in medical image segmentation, where reference annotations are diff…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel +4
A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurren…
Towards annotation-efficient segmentation via image-to-image translation
Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham +2
Often in medical imaging, it is prohibitively challenging to produce enough boundary annotations to train deep neural networks for accurate tumor segmentation. We propose the use o…