mlsanityData to 7 Oct 2026

Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images

arXiv 2205.15530 ↗First posted 31 May 2022cs.CVcited by 3

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Abstract (arXiv)

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models. Specifically, the pseudo histopathological images are generated from each center, which contains inherent and specific properties corresponding to the real images in this center, but does not include the privacy information. These pseudo images are then shared in the central server for self-supervised learning (SSL). A multi-task SSL is then designed to fully learn both the center-specific information and common inherent representation according to the data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm is proposed to improve the local training for the CAD model in each center by conducting contrastive learning, which benefits the optimization of the global model in the FL procedure. The experimental results on three public histopathological image datasets indicate the effectiveness of the proposed SSL-FL-BT on both diagnostic accuracy and generalization.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 42 of its 91 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

42 of 91 references.

References · 42

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30 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023SelfFed: Self-Supervised Federated Learning for Data Heterogeneity and Label Scarcity in Medical Imagescited by 4
  2. 2025Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detectioncited by 4
  3. 2023FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classificationcited by 3