PathFL: Multi-alignment Federated Learning for pathology image segmentation.

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.