A Federated and Explainable Deep Learning Framework for Multi-Institutional Cancer Diagnosis

Abstract

The accurate and early diagnosis of cancer is pivotal for improving patient outcomes, yet it faces significant challenges related to data privacy, institutional silos, and the "black-box" nature of advanced deep learning models. While centralized deep learning has demonstrated remarkable diagnostic performance, its development is often hampered by the inability to pool sensitive medical data from multiple institutions due to stringent privacy regulations like HIPAA and GDPR. Federated learning (FL) emerges as a promising paradigm that enables collaborative model training without sharing local data. However, the integration of FL in clinical settings is impeded by its inherent lack of model interpretability, which is crucial for gaining the trust of clinicians and complying with medical standards. This paper proposes a novel federated and explainable deep learning framework designed for multi-institutional cancer diagnosis. Our approach leverages a robust federated averaging algorithm to train a centralized model on distributed datasets across different hospitals, ensuring data privacy. Furthermore, we integrate state-of-the-art explainable AI (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM), to generate intuitive visual explanations for the model's predictions. We validate our framework on a large-scale, multi-institutional dataset of histopathological and radiological images, demonstrating that it achieves diagnostic accuracy comparable to a centralized model while providing transparent, clinically actionable insights. This work bridges the critical gaps of data privacy and model interpretability, paving the way for the widespread, trustworthy adoption of AI in collaborative cancer diagnostics.