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AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation

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arxiv 2505.02830 v1 pith:PU4MZ2PB submitted 2025-05-05 cs.CV cs.CL

AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation

Qingqiu Li , Zihang Cui , Seongsu Bae , Jilan Xu , Runtian Yuan , Yuejie Zhang , Rui Feng , Quanli Shen
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Xiaobo Zhang Junjun He Shujun Wang
This is my paper
classification cs.CV cs.CL
keywords reasoninglargemlmmsaccuracyanatomicalchestinterpretationlmms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Large Multimodal Models (LMMs) have enabled automated CXR interpretation, enhancing diagnostic accuracy and efficiency. However, despite their strong visual understanding, current Medical LMMs (MLMMs) still face two major challenges: (1) Insufficient region-level understanding and interaction, and (2) Limited accuracy and interpretability due to single-step reasoning. In this paper, we empower MLMMs with anatomy-centric reasoning capabilities to enhance their interactivity and explainability. Specifically, we first propose an Anatomical Ontology-Guided Reasoning (AOR) framework, which centers on cross-modal region-level information to facilitate multi-step reasoning. Next, under the guidance of expert physicians, we develop AOR-Instruction, a large instruction dataset for MLMMs training. Our experiments demonstrate AOR's superior performance in both VQA and report generation tasks.

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Cited by 4 Pith papers

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  2. AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation

    cs.CV 2026-01 conditional novelty 7.0

    AnatomiX, a two-stage anatomy-first multimodal LLM for chest X-ray interpretation, reports >25% relative gains on anatomy grounding and grounded captioning, but some aggregate benchmark numbers are internally inconsis...

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  4. Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models

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    Context alignment in medical VLMs raises AUC from 0.918 to 0.925, cuts hallucinated keywords from 1.14 to 0.25, shortens explanations to 15.3 words, and maintains calibrated uncertainty without raising model confidence.