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Med-CAM is introduced as a framework for generating minimal and sharp evidence-based explanations for medical image classification, addressing the black-box nature of deep learning in medical AI. It trains a segmentation network to produce masks highlighting the minimal evidence crucial for a model's decision, ensuring faithfulness and interpretability. Experiments demonstrate that Med-CAM outperforms existing methods like Grad-CAM by providing conclusive, evidence-based explanations that faithfully replicate the model's prediction, enhancing clinician trust in high-stakes medical applications.
Fuzzy heatmaps are out: Med-CAM delivers crisp, minimal evidence maps that clinicians can actually use to understand AI decisions in medical imaging.
Reliable and interpretable decision-making is essential in medical imaging, where diagnostic outcomes directly influence patient care. Despite advances in deep learning, most medical AI systems operate as opaque black boxes, providing little insight into why a particular diagnosis was reached. In this paper, we introduce Med-CAM, a framework for generating minimal and sharp maps as evidence-based explanations for Medical decision making via Classifier Activation Matching. Med-CAM trains a segmentation network from scratch to produce a mask that highlights the minimal evidence critical to model's decision for any seen or unseen image. This ensures that the explanation is both faithful to the network's behaviour and interpretable to clinicians. Experiments show, unlike prior spatial explanation methods, such as Grad-CAM and attention maps, which yield only fuzzy regions of relative importance, Med-CAM with its superior spatial awareness to shapes, textures, and boundaries, delivers conclusive, evidence-based explanations that faithfully replicate the model's prediction for any given image. By explicitly constraining explanations to be compact, consistent with model activations, and diagnostic alignment, Med-CAM advances transparent AI to foster clinician understanding and trust in high-stakes medical applications such as pathology and radiology.