Learning from the Right Regions: Class-Conditioned Explanation Guidance for Medical Imaging

Key points

• Deep neural networks can achieve strong predictive performance in medical imaging while relying on regions unrelated to the predicted finding.1
• We propose a class-conditioned explanation-guided learning framework that constructs a separate explanation for each positive class and penalizes evidence outside its class-specific annotated regions.1
• We instantiate the framework using Gradient-weighted Class Activation Mapping (Grad-CAM) and evaluate it on 14 abnormalities using EfficientNet-B1, ViT-B/16, ViT-B/32, and Swin-B.1
• Our results show that class-conditioned explanation guidance can substantially improve where multi-label classifiers concentrate their explanatory evidence while largely preserving predictive performance.1

Sources (1)

• [1]Learning from the Right Regions: Class-Conditioned Explanation Guidance for Medical Imaging
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 02:27 AM

  Explanation-guided learning can address this problem, but multi-label classification introduces a key challenge: multiple abnormalities may coexist in an image, requiring each prediction to align with its corresponding spatial evidence.

  Deep neural networks can achieve strong predictive performance in medical imaging while relying on regions unrelated to the predicted finding.

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