DWARF: Disease-weighted network for attention map refinement

TL;DR

DWARF is a novel deep learning framework that incorporates medical expert feedback to refine attention maps, significantly improving interpretability and diagnostic accuracy in medical imaging.

Contribution

The paper introduces DWARF, a disease-weighted attention map refinement network that uses cyclic training with expert feedback to enhance interpretability and performance.

Findings

Improved interpretability of attention maps.

Enhanced diagnostic accuracy across datasets.

Effective collaboration between AI and medical professionals.

Abstract

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