Federated Learning Pipeline for Whole-Slide Image Classification in Digital Neuropathology.

چکیده اصلی

Federated learning (FL) has emerged as a powerful paradigm for privacy-preserving, collaborative training of machine learning models, particularly valuable in fields like digital pathology, where data sharing is constrained by institutional policies and regulatory concerns. This chapter presents a reproducible computational pipeline for whole-slide image (WSI) classification in digital neuropathology using a federated learning framework. In addition to outlining the end-to-end implementation, including preprocessing, model training, and deployment, we highlight key challenges specific to applying FL in WSI analysis, such as data heterogeneity and communication efficiency. Detailed code snippets, implementation guidance, and deployment recommendations are provided to support real-world adoption. We hope this chapter serves as a valuable resource for neuropathologists, researchers, and machine learning practitioners aiming to bridge technology and medical science in the evolving landscape of digital neuropathology.

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