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

• URL: http://arxiv.org/abs/2108.09451v1
• Date: Sat, 21 Aug 2021 07:29:13 GMT
• Title: Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model
• Authors: Kwanseok Oh, Jee Seok Yoon, and Heung-Il Suk
• Abstract summary: We propose a novel framework that unifies diagnostic model learning, visual explanation generation, and trained diagnostic model reinforcement. For the visual explanation, we generate a counterfactual map that transforms an input sample to be identified as a target label.
• Score: 1.6287500717172143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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