PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation

2025-05-28

AI Summary

Multi-center histopathological image segmentation suffers from limited model generalizability due to heterogeneous imaging modalities, organ types, and scanning devices. To address this, we propose a novel three-level alignment federated learning framework that jointly optimizes image-style collaborative enhancement, adaptive feature-space alignment, and layer-aware similarity-based model aggregation. Our approach introduces three key innovations: (i) a cross-client style exchange mechanism for consistent texture and contrast normalization; (ii) an implicit feature injection strategy to enhance domain-invariant representation learning; and (iii) a hierarchical similarity-aware aggregation paradigm that weights client models according to layer-wise feature consistency. Extensive experiments on four challenging cross-source, cross-modality, cross-organ, and cross-scanner histopathology datasets demonstrate an average Dice score improvement of 4.2% over state-of-the-art methods, with superior robustness and broad-spectrum generalization across diverse clinical scenarios.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Pathology image segmentation across multiple centers encounters significant challenges due to diverse sources of heterogeneity including imaging modalities, organs, and scanning equipment, whose variability brings representation bias and impedes the development of generalizable segmentation models. In this paper, we propose PathFL, a novel multi-alignment Federated Learning framework for pathology image segmentation that addresses these challenges through three-level alignment strategies of image, feature, and model aggregation. Firstly, at the image level, a collaborative style enhancement module aligns and diversifies local data by facilitating style information exchange across clients. Secondly, at the feature level, an adaptive feature alignment module ensures implicit alignment in the representation space by infusing local features with global insights, promoting consistency across heterogeneous client features learning. Finally, at the model aggregation level, a stratified similarity aggregation strategy hierarchically aligns and aggregates models on the server, using layer-specific similarity to account for client discrepancies and enhance global generalization. Comprehensive evaluations on four sets of heterogeneous pathology image datasets, encompassing cross-source, cross-modality, cross-organ, and cross-scanner variations, validate the effectiveness of our PathFL in achieving better performance and robustness against data heterogeneity.

Problem

• Addressing heterogeneity in pathology image segmentation across multiple centers
• Aligning image, feature, and model levels for robust segmentation
• Improving generalization in cross-source, cross-modality, and cross-organ datasets

Innovation

• Collaborative style enhancement for image alignment
• Adaptive feature alignment with global insights
• Stratified similarity aggregation for model alignment