Interpretable Deep Learning Framework for Improved Disease Classification in Medical Imaging

ArXi:2503.11851v3 Announce Type: replace-cross Deep learning models have gained increasing adoption in medical image analysis. However, these models often produce overconfident predictions, which can compromise clinical accuracy and reliability. Bridging the gap between high-performance and awareness of uncertainty remains a crucial challenge in biomedical imaging applications. This study focuses on developing a unified deep learning framework for enhancing feature integration, interpretability, and reliability in prediction. We