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The ambiguity of the decision-making process has been pointed out as the mainobstacle to applying the deep learning-based method in a practical way in spiteof its outstanding performance. Interpretability could guarantee the confidenceof deep learning system, therefore it is particularly important in the medicalfield. In this study, a novel deep network is proposed to explain thediagnostic decision with visual pointing map and diagnostic sentence justifyingresult simultaneously. For the purpose of increasing the accuracy of sentencegeneration, a visual word constraint model is devised in training justificationgenerator. To verify the proposed method, comparative experiments wereconducted on the problem of the diagnosis of breast masses. Experimentalresults demonstrated that the proposed deep network could explain diagnosismore accurately with various textual justifications.

Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis