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 cannot simultaneously
exploit local context information and long-range dependencies in the site-end
feature representation of federated learning. To address these issues, we
present a point transformer with federated learning for multi-site HER2 status
prediction from HE-stained WSIs. Our approach incorporates two novel designs.
We propose a dynamic label distribution strategy and an auxiliary classifier,
which helps to establish a well-initialized model and mitigate label
distribution variations across sites. Additionally, we propose a farthest
cosine sampling based on cosine distance. It can sample the most distinctive
features and capture the long-range dependencies. Extensive experiments and
analysis show that our method achieves state-of-the-art performance at four
sites with a total of 2687 WSIs. Furthermore, we demonstrate that our model can
generalize to two unseen sites with 229 WSIs.

通过使用点变换器与联邦学习方法对多地点 HER2 状态进行预测，我们提出了两个新的设计：动态标签分布策略和辅助分类器，它们有助于建立可初始化的模型和减轻不同地点标签分布的变化；此外，我们还提出了基于余弦距离的最远余弦采样，能够选择最具特色的特征并捕捉长程依赖关系。广泛的实验和分析表明，我们的方法在 4 个地点的 2687 个 WSIs 上实现了最先进的性能，并且我们证明了我们的模型可以泛化到两个未见过的地点，共 229 个 WSIs。