arXiv AI September 15, 2026 By Xinyue Xu, Hongbin Lin, Juangui Xu, Hualiang Wang, Lehan Wang, Lijie Hu, Weiyang Liu, Adrian Weller, Xiaomeng Li

Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation

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The paper introduces CORAL, a multimodal framework that combines spatial grounding and concept-level supervision for medical report generation. CORAL uses a prompt-driven segmentation model to localize lesions and a Concept Bottleneck module to predict multi-class clinical attributes, feeding these textual concept tokens and mask-modulated visual features into a multimodal large language model. Experiments on BUS-CoT and IU X-ray datasets show that CORAL improves diagnostic accuracy, concept consistency, and report quality compared to existing general-purpose and medical MLLMs.

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