  The interpretability of deep learning is crucial for evaluating the
reliability of medical imaging models and reducing the risks of inaccurate
patient recommendations. This study addresses the "human out of the loop" and
"trustworthiness" issues in medical image analysis by integrating medical
professionals into the interpretability process. We propose a disease-weighted
attention map refinement network (DWARF) that leverages expert feedback to
enhance model relevance and accuracy. Our method employs cyclic training to
iteratively improve diagnostic performance, generating precise and
interpretable feature maps. Experimental results demonstrate significant
improvements in interpretability and diagnostic accuracy across multiple
medical imaging datasets. This approach fosters effective collaboration between
AI systems and healthcare professionals, ultimately aiming to improve patient
outcomes
