Weakly Supervised Concept Learning with Class-Level Priors for Interpretable Medical Diagnosis

• URL: http://arxiv.org/abs/2511.01131v1
• Date: Mon, 03 Nov 2025 00:43:04 GMT
• Title: Weakly Supervised Concept Learning with Class-Level Priors for Interpretable Medical Diagnosis
• Authors: Md Nahiduzzaman, Steven Korevaar, Alireza Bab-Hadiashar, Ruwan Tennakoon,
• Abstract summary: Prior-guided Concept Predictor (PCP) is a weakly supervised framework that enables concept answer prediction without explicit supervision or reliance on language models.<n>PCP improves concept-level F1-score by over 33% compared to zero-shot baselines.
• Score: 9.002305517166635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-interpretable predictions are essential for deploying AI in medical imaging, yet most interpretable-by-design (IBD) frameworks require concept annotations for training data, which are costly and impractical to obtain in clinical contexts. Recent attempts to bypass annotation, such as zero-shot vision-language models or concept-generation frameworks, struggle to capture domain-specific medical features, leading to poor reliability. In this paper, we propose a novel Prior-guided Concept Predictor (PCP), a weakly supervised framework that enables concept answer prediction without explicit supervision or reliance on language models. PCP leverages class-level concept priors as weak supervision and incorporates a refinement mechanism with KL divergence and entropy regularization to align predictions with clinical reasoning. Experiments on PH2 (dermoscopy) and WBCatt (hematology) show that PCP improves concept-level F1-score by over 33% compared to zero-shot baselines, while delivering competitive classification performance on four medical datasets (PH2, WBCatt, HAM10000, and CXR4) relative to fully supervised concept bottleneck models (CBMs) and V-IP.

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