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Existing studies on disease diagnostic models focus either on diagnosticmodel learning for performance improvement or on the visual explanation of atrained diagnostic model. We propose a novel learn-explain-reinforce (LEAR)framework that unifies diagnostic model learning, visual explanation generation(explanation unit), and trained diagnostic model reinforcement (reinforcementunit) guided by the visual explanation. For the visual explanation, we generatea counterfactual map that transforms an input sample to be identified as anintended target label. For example, a counterfactual map can localizehypothetical abnormalities within a normal brain image that may cause it to bediagnosed with Alzheimer's disease (AD). We believe that the generatedcounterfactual maps represent data-driven and model-induced knowledge about atarget task, i.e., AD diagnosis using structural MRI, which can be a vitalsource of information to reinforce the generalization of the trained diagnosticmodel. To this end, we devise an attention-based feature refinement module withthe guidance of the counterfactual maps. The explanation and reinforcementunits are reciprocal and can be operated iteratively. Our proposed approach wasvalidated via qualitative and quantitative analysis on the ADNI dataset. Itscomprehensibility and fidelity were demonstrated through ablation studies andcomparisons with existing methods.

Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model