A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN

• Published in: IEEE transactions on medical imaging Vol. 42; no. 7; pp. 1969 - 1981
• Main Authors: Shen, Yiqing, Sowmya, Arcot, Luo, Yulin, Liang, Xiaoyao, Shen, Dinggang, Ke, Jing
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Cancer; Classification; Computer networks; Cybersecurity; Distillation; Federated learning; generative adversarial network; Generative adversarial networks; Generators; Histopathology; Humans; Image analysis; Image classification; Image processing; Image Processing, Computer-Assisted; Machine Learning; Privacy; Regularization; Servers; stain normalization; Stains; Training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2022.3221724

Currently, data-driven based machine learning is considered one of the best choices in clinical pathology analysis, and its success is subject to the sufficiency of digitized slides, particularly those with deep annotations. Although centralized training on a large data set may be more reliable and more generalized, the slides to the examination are more often than not collected from many distributed medical institutes. This brings its own challenges, and the most important is the assurance of privacy and security of incoming data samples. In the discipline of histopathology image, the universal stain-variation issue adds to the difficulty of an automatic system as different clinical institutions provide distinct stain styles. To address these two important challenges in AI-based histopathology diagnoses, this work proposes a novel conditional Generative Adversarial Network (GAN) with one orchestration generator and multiple distributed discriminators, to cope with multiple-client based stain-style normalization. Implemented within a Federated Learning (FL) paradigm, this framework well preserves data privacy and security. Additionally, the training consistency and stability of the distributed system are further enhanced by a novel temporal self-distillation regularization scheme. Empirically, on large cohorts of histopathology datasets as a benchmark, the proposed model matches the performance of conventional centralized learning very closely. It also outperforms state-of-the-art stain-style transfer methods on the downstream Federated Learning image classification task, with an accuracy increase of over 20.0% in comparison to the baseline classification model.