The Federated Tumor Segmentation (FeTS) Challenge

Sarthak Pati; Ujjwal Baid; Maximilian Zenk; Brandon Edwards; Micah Sheller; G. Anthony Reina; Patrick Foley; Alexey Gruzdev; Jason Martin; Shadi Albarqouni; Yong Chen; Russell Taki Shinohara; Annika Reinke; David Zimmerer; John B. Freymann; Justin S. Kirby; Christos Davatzikos; Rivka R. Colen; Aikaterini Kotrotsou; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Hassan Fathallah-Shaykh; Roland Wiest; Andras Jakab; Marc-Andre Weber; Abhishek Mahajan; Lena Maier-Hein; Jens Kleesiek; Bjoern Menze; Klaus Maier-Hein; Spyridon Bakas

arXiv· arXiv:2105.05874· Sarthak Pati, Ujjwal Baid, Maximilian Zenk, Brandon Edwards, Micah Sheller, G. Anthony Reina, Patrick Foley, Alexey Gruzdev, Jason Martin, Shadi Albarqouni, Yong Chen, Russell Taki Shinohara, Annika Reinke, David Zimmerer, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Lena Maier-Hein, Jens Kleesiek, Bjoern Menze, Klaus Maier-Hein, Spyridon Bakas·本站收录2026-10-28 01:54· 原文发表2021年5月13日

联邦肿瘤分割（FeTS）挑战赛

The Federated Tumor Segmentation (FeTS) Challenge

AI 导读

FeTS 2021 挑战赛聚焦联邦学习下的胶质瘤分割，设两项任务：寻找最优权重聚合方法训练共识模型，以及跨机构泛化评估。它使用 BraTS 2020 的临床多机构 MRI 扫描及真实联邦协作网络中远程独立机构的数据，训练时数据始终保留在各机构。泛化评估在未参与训练的机构数据分布上进行，考察脑肿瘤分割模型在真实场景中的表现。

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