1.3k citations · 3.6k across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson, Michela Antonelli, Spyridon Bakas +21
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is c…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…
Liver lesion segmentation informed by joint liver segmentation
Eugene Vorontsov, An Tang, Chris Pal +1
We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes. We build the model from two fully convolutional networks, connecte…