Feb. 28, 2024, 5:44 a.m. | Bao Li, Zhenyu Liu, Lizhi Shao, Bensheng Qiu, Hong Bu, Jie Tian

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.06454v3 Announce Type: replace-cross
Abstract: Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables collaborative training of these WSIs without gigabyte-size WSIs transportation and data privacy concerns. However, federated learning encounters challenges in addressing label imbalance in multi-site WSIs from the real world. Moreover, existing WSI classification methods …

abstract arxiv cancer costs cs.cv cs.lg eess.iv federated learning growth human images reduce technical transformer treatment type

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