{"slug": "beyond-explanation-debugging-medical-imaging-models-via-concept-intervention", "title": "Beyond Explanation: Debugging Medical Imaging Models via Concept Intervention", "summary": "A new arXiv paper (2610.09031v1) introduces a plug-and-play framework for concept-based interpretation and refinement of medical imaging models, building a Concept Bottleneck Model by aligning a single-modality encoder to BioMedCLIP. Evaluated on a Mayo Clinic ultrasound dataset and the CheXpert 5x200 chest X-ray dataset, the framework's concept-level interventions isolate causal versus spuriously correlated concepts and generate counterfactual samples for targeted fine-tuning, maintaining or occasionally improving predictive performance. The authors report the approach enables reliable model diagnosis for controlled, interpretable refinement of clinical deep learning models.", "body_md": "arXiv:2610.09031v1 Announce Type: new \nAbstract: Medical imaging models often operate as black boxes, limiting interpretability and systematic debugging. We introduce an easy-to-use, plug-and-play framework for concept-based interpretation and model refinement. By aligning a single-modality encoder to BioMedCLIP, we construct a Concept Bottleneck Model (CBM) that enables concept-level interventions. These interventions allow us to isolate causal versus spuriously correlated concepts, validate insights with domain experts, and generate counterfactual samples for targeted fine-tuning. We evaluate our framework on a Mayo Clinic ultrasound dataset and the CheXpert 5x200 chest X-ray dataset. Results demonstrate that concept intervention enables reliable model diagnosis while maintaining, and occasionally improving predictive performance via guided fine-tuning. Our findings highlight the practical value of this framework for controlled, interpretable refinement of clinical deep learning models.", "url": "https://wpnews.pro/news/beyond-explanation-debugging-medical-imaging-models-via-concept-intervention", "canonical_source": "https://arxiv.org/abs/2610.09031", "published_at": "2026-10-08 04:00:00+00:00", "updated_at": "2026-10-08 04:19:53.089428+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research", "computer-vision", "ai-safety"], "entities": ["BioMedCLIP", "Mayo Clinic", "CheXpert", "Concept Bottleneck Model", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/beyond-explanation-debugging-medical-imaging-models-via-concept-intervention", "markdown": "https://wpnews.pro/news/beyond-explanation-debugging-medical-imaging-models-via-concept-intervention.md", "text": "https://wpnews.pro/news/beyond-explanation-debugging-medical-imaging-models-via-concept-intervention.txt", "jsonld": "https://wpnews.pro/news/beyond-explanation-debugging-medical-imaging-models-via-concept-intervention.jsonld"}}