2 code implementations • 17 May 2022 • Florian Kofler, Suprosanna Shit, Ivan Ezhov, Lucas Fidon, Izabela Horvath, Rami Al-Maskari, Hongwei Li, Harsharan Bhatia, Timo Loehr, Marie Piraud, Ali Erturk, Jan Kirschke, Jan C. Peeken, Tom Vercauteren, Claus Zimmer, Benedikt Wiestler, Bjoern Menze
\emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters.
1 code implementation • 31 Aug 2023 • Chinmay Prabhakar, Hongwei Bran Li, Johannes C. Paetzold, Timo Loehr, Chen Niu, Mark Mühlau, Daniel Rueckert, Benedikt Wiestler, Bjoern Menze
We propose a two-stage MS inflammatory disease activity prediction approach.
no code implementations • 25 Jan 2020 • Hongwei Li, Timo Loehr, Anjany Sekuboyina, Jian-Guo Zhang, Benedikt Wiestler, Bjoern Menze
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e. g. a new centreor a new scanner.
no code implementations • 16 Nov 2021 • Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Benedikt Wiestler, Bjoern Menze
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community.