Search papers, labs, and topics across Lattice.
Cancer Research Center, University of Heidelberg, Heidelberg University Hospital
4
0
4
A new open-source framework transforms how multi-center radiology studies monitor progress and explore data, enabling real-time insights and efficient collaboration.
Real-world label noise can significantly hinder federated learning in medical image segmentation, but our benchmark suite offers a comprehensive solution for evaluating FNLL methods under realistic conditions.
TwinTrack transforms ambiguous medical image segmentation by calibrating model outputs to reflect the true consensus of expert annotators.
Simply averaging pixel-level uncertainty in image segmentation throws away crucial spatial information, leading to worse performance on downstream tasks like detecting when your model is likely to fail.
