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

1Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.

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 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. Code is available at: https://github.com/boyden/PointTransformerFL

References Powered by Scopus

Feature pyramid networks for object detection

19893Citations
N/AReaders
Get full text

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

16599Citations
N/AReaders
Get full text

PointNet: Deep learning on point sets for 3D classification and segmentation

10872Citations
N/AReaders
Get full text

Cited by Powered by Scopus

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

2Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Li, B., Liu, Z., Shao, L., Qiu, B., Bu, H., & Tian, J. (2024). Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, pp. 3000–3008). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v38i4.28082

Readers' Seniority

Tooltip

Professor / Associate Prof. 3

60%

PhD / Post grad / Masters / Doc 2

40%

Readers' Discipline

Tooltip

Computer Science 5

83%

Biochemistry, Genetics and Molecular Bi... 1

17%

Save time finding and organizing research with Mendeley

Sign up for free