XPA-FedCBR: A Federated Case-Based Reasoning System for Explainable and Privacy-Aware Lung Cancer Diagnosis

Abstract

Lung cancer is among the deadliest cancers worldwide, largely because it is usually caught too late. Building AI tools for earlier, stage-aware diagnosis is hard in practice: patient scans are scattered across hospitals that cannot share them freely, and clinicians are reluctant to trust models that cannot explain their reasoning. This study presents XPA-FedCBR, a framework that addresses both concerns. It combines Federated Learning, which trains a shared model across institutions without moving raw data, with Case-Based Reasoning, which supports each prediction by retrieving clinically similar past cases and, for uncertain predictions, using them to refine the decision. We evaluate it on the LIDC-IDRI dataset (roughly 1,018 CT scans from 1,010 patients), split across five simulated institutions under non-IID conditions, with annotations mapped to stages I–IV. Over four independent runs, the proposed model reaches a macro-F1 of 0.915, significantly ahead of FedAvg without CBR (0.875), a centralized CNN (0.895), and a standalone CNN (0.844), with its advantage largest under domain shift. Its case-based explanations, evaluated quantitatively and against gradient-based saliency, give clinicians evidence they can directly inspect.