AI RESEARCH
Explainability in Generative Medical Diffusion Models: A Faithfulness-Based Analysis on MRI Synthesis
arXiv CS.LG
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ArXi:2602.09781v2 Announce Type: replace This study investigates the explainability of generative diffusion models in the context of medical imaging, focusing on Magnetic resonance imaging (MRI) synthesis. Although diffusion models have shown strong performance in generating realistic medical images, their internal decision making process remains largely opaque. We present a faithfulness-based explainability framework that analyzes how prototype-based explainability methods like ProtoPNet (PPNet), Enhanced ProtoPNet (EPPNet), and ProtoPool can link the relationship between generated and.