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PathFL: Multi-alignment Federated Learning for pathology image segmentation
Yuan Zhang1, Feng Chen2, Yaolei Qi1
1Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, No. 2, Sipai Lou, Xuanwu District, Nanjing, 210096, China.
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. The code is available at https://github.com/yuanzhang7/PathFL.
Insights
PathFL, a novel federated learning framework, enhances pathology image segmentation by aligning data, features, and models across diverse datasets. This approach improves generalization and robustness against heterogeneity in imaging and equipment.
Area of Science:
- Digital Pathology
- Medical Image Analysis
- Artificial Intelligence
Background:
- Pathology image segmentation faces challenges due to data heterogeneity from various sources like imaging modalities and equipment.
- This heterogeneity leads to representation bias, hindering the development of generalizable segmentation models.
Purpose of the Study:
- To propose PathFL, a multi-alignment Federated Learning (FL) framework for robust pathology image segmentation.
- To address data heterogeneity and improve the generalizability of segmentation models in diverse pathological imaging scenarios.
Main Methods:
- PathFL employs a three-level alignment strategy: image-level collaborative style enhancement, feature-level adaptive feature alignment, and model-level stratified similarity aggregation.
- Image-level alignment facilitates style information exchange for data diversification.
- Feature-level alignment infuses local features with global insights for representation consistency.
- Model-level aggregation uses layer-specific similarity to account for client discrepancies.
Main Results:
- PathFL demonstrated superior performance and robustness across four heterogeneous pathology image datasets.
- Evaluations included cross-source, cross-modality, cross-organ, and cross-scanner variations.
- The framework effectively mitigates representation bias caused by data heterogeneity.
Conclusions:
- PathFL offers an effective solution for pathology image segmentation in heterogeneous multi-center settings.
- The proposed multi-alignment strategies significantly enhance model generalization and robustness against diverse data variations.
- The framework shows promise for developing reliable AI tools in digital pathology.
