Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
This study introduces a federated learning-based approach to predict HER2
status from hematoxylin and eosin (HE)-stained whole slide images (WSIs),
reducing costs and speeding up treatment decisions. To address label imbalance
and feature representation challenges in multisite datasets, a point
transformer is proposed, incorporating dynamic label distribution, an auxiliary
classifier, and farthest cosine sampling. Extensive experiments demonstrate
state-of-the-art performance across four sites (2687 WSIs) and strong
generalization to two unseen sites (229 WSIs).
