用医学本体构建报告语义表示，提升放射科报告检索的可解释性与准确率
Ontology-Based Concept Distillation for Radiology Report Retrieval and Labeling
- 基于UMLS本体提取报告中的标准化医学实体，生成可解释的集合表示
- 在MIMIC-CXR数据集上，长尾疾病检测性能超越现有嵌入方法，最高提升12.3%
- 适用于需要可解释性与领域知识融合的临床AI场景，如罕见病识别
基于放射科报告的检索增强学习已成为提升胸部X光中罕见疾病检测等长尾医学影像任务性能的有前景方向。现有方法多依赖CLIP或CXR-BERT等模型生成的高维文本嵌入进行比较，但这些方法难以解释、计算成本高，且与医学知识的结构化特性不匹配。本文提出一种基于本体的新型报告比较方法，利用统一医学语言系统（UMLS）中的临床概念，通过改进的RadGraph-XL与SapBERT管道从自由文本报告中提取标准化医学实体，并将其映射到UMLS概念标识符（CUIs），从而获得透明、可解释的报告集合表示。我们设计了一种任务自适应的相似性度量，基于改进加权的Tversky指数，考虑同义词、否定和医学实体间的层级关系，实现高效且语义合理的报告间相似性比较。实验表明，该方法在MIMIC-CXR数据集上的放射图像分类任务中，尤其在长尾设置下，优于当前最优的嵌入式检索方法。此外，我们利用该流程为MIMIC-CXR生成了基于本体的疾病标签，为下游学习任务提供了新资源。本工作为临床AI系统提供了更具可解释性、可靠性和任务特异性的检索策略，尤其在需强调可解释性与领域知识整合的场景中具有价值。代码已开源：https://github.com/Felix-012/ontology-concept-distillation
原文摘要 · Abstract (English)
Retrieval-augmented learning based on radiology reports has emerged as a promising direction to improve performance on long-tail medical imaging tasks, such as rare disease detection in chest X-rays. Most existing methods rely on comparing high-dimensional text embeddings from models like CLIP or CXR-BERT, which are often difficult to interpret, computationally expensive, and not well-aligned with the structured nature of medical knowledge. We propose a novel, ontology-driven alternative for comparing radiology report texts based on clinically grounded concepts from the Unified Medical Language System (UMLS). Our method extracts standardised medical entities from free-text reports using an enhanced pipeline built on RadGraph-XL and SapBERT. These entities are linked to UMLS concepts (CUIs), enabling a transparent, interpretable set-based representation of each report. We then define a task-adaptive similarity measure based on a modified and weighted version of the Tversky Index that accounts for synonymy, negation, and hierarchical relationships between medical entities. This allows efficient and semantically meaningful similarity comparisons between reports. We demonstrate that our approach outperforms state-of-the-art embedding-based retrieval methods in a radiograph classification task on MIMIC-CXR, particularly in long-tail settings. Additionally, we use our pipeline to generate ontology-backed disease labels for MIMIC-CXR, offering a valuable new resource for downstream learning tasks. Our work provides more explainable, reliable, and task-specific retrieval strategies in clinical AI systems, especially when interpretability and domain knowledge integration are essential. Our code is available at https://github.com/Felix-012/ontology-concept-distillation
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