Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging

ArXi:2605.10054v1 Announce Type: new Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues, limiting their trustworthiness in practice. Post-hoc explanation methods are widely used to visualize model decisions in the form of saliency maps; however, these explanations do not influence how models, allowing non-causal or confounding features to persist. This motivates the incorporation of explanation supervision directly into the.