1.2k citations · 1.4k across the 7 of their papers we have counts for
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Revisiting MAE pre-training for 3D medical image segmentation
Tassilo Wald, Constantin Ulrich, Stanislav Lukyanenko +6
Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from th…
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…
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Tobias Ross, David Zimmerer, Anant Vemuri +12
Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to t…