Personalized stain style transfer layers for distributed histology classification

Yiqing Shen

doi:10.1117/12.2607168

4 April 2022 Personalized stain style transfer layers for distributed histology classification

Yiqing Shen

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120390M (2022) https://doi.org/10.1117/12.2607168
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Federated learning (FL) contributed to the popularity of using multi-centric and collaborative integration of decentralized data across multiple institutions to train a robust model. Yet, data heterogeneity among the distributed clients remains a big challenge in FL. Specifically, in the case of digital histology, stain variation is commonplace. In a collaboration setting, color normalization and data augmentation are adopted to alleviate the variation. But they cannot be directly applied in the FL paradigm, as the requirement of data sharing or the availability of proxy samples. To address this issue, we propose a novel personalized federated learning (PFL) approach. Personalization stain transfer layers learn to project stain variant input images into a homogeneous space before being fed to the FL backbone. The proposed method is simple yet efficient. Empirical results on public available large patient cohorts demonstrate an observable classification accuracy improvement on popular neural network architectures including ResNet, Vgg, Wide ResNet.

Conference Presentation

Citation Download Citation

Yiqing Shen "Personalized stain style transfer layers for distributed histology classification", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120390M (4 April 2022); https://doi.org/10.1117/12.2607168

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