English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Directly predicting human epidermal growth factor receptor 2 (HER2) statusfrom widely available hematoxylin and eosin (HE)-stained whole slide images(WSIs) can reduce technical costs and expedite treatment selection. Accuratelypredicting HER2 requires large collections of multi-site WSIs. Federatedlearning enables collaborative training of these WSIs without gigabyte-sizeWSIs transportation and data privacy concerns. However, federated learningencounters challenges in addressing label imbalance in multi-site WSIs from thereal world. Moreover, existing WSI classification methods cannot simultaneouslyexploit local context information and long-range dependencies in the site-endfeature representation of federated learning. To address these issues, wepresent a point transformer with federated learning for multi-site HER2 statusprediction 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 labeldistribution variations across sites. Additionally, we propose a farthestcosine sampling based on cosine distance. It can sample the most distinctivefeatures and capture the long-range dependencies. Extensive experiments andanalysis show that our method achieves state-of-the-art performance at foursites with a total of 2687 WSIs. Furthermore, we demonstrate that our model cangeneralize to two unseen sites with 229 WSIs.

Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images