Search Results for author: Timo Loehr

Found 3 papers, 1 papers with code

FedCostWAvg: A new averaging for better Federated Learning

no code implementations16 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.

Federated Learning Tumor Segmentation

Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns

no code implementations25 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.

Image Segmentation Lesion Segmentation +2

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