  Existing studies on disease diagnostic models focus either on diagnostic
model learning for performance improvement or on the visual explanation of a
trained 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 (reinforcement
unit) guided by the visual explanation. For the visual explanation, we generate
a counterfactual map that transforms an input sample to be identified as an
intended target label. For example, a counterfactual map can localize
hypothetical abnormalities within a normal brain image that may cause it to be
diagnosed with Alzheimer's disease (AD). We believe that the generated
counterfactual maps represent data-driven and model-induced knowledge about a
target task, i.e., AD diagnosis using structural MRI, which can be a vital
source of information to reinforce the generalization of the trained diagnostic
model. To this end, we devise an attention-based feature refinement module with
the guidance of the counterfactual maps. The explanation and reinforcement
units are reciprocal and can be operated iteratively. Our proposed approach was
validated via qualitative and quantitative analysis on the ADNI dataset. Its
comprehensibility and fidelity were demonstrated through ablation studies and
comparisons with existing methods.
