重新思考医学报告生成：基于知识图谱的疾病揭示增强
Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph
AI总结：
针对医学报告生成中数据集疾病分布的长尾问题，本研究构建含137类疾病的胸部X线知识图谱，提出两阶段生成框架与尾部疾病增强策略，并设计DS指标，有效提升了生成报告的疾病匹配准确性与多样性。
摘要：
知识图谱（Knowledge Graph, KG）在医学报告生成（Medical Report Generation, MRG）中发挥着关键作用，因为它能够揭示疾病之间的关联，因此可用于指导生成过程。然而，构建全面的KG需要大量人力，且其在MRG流程中的应用仍有待深入探索。本研究构建了一个完整的胸部X线影像KG，涵盖137种疾病和异常表现。基于该KG，我们发现当前的MRG数据集在疾病分布上存在长尾问题。为缓解这一问题，我们提出了一种新型增强策略，用于强化分布尾部疾病类型的表征。我们进一步设计了一种两阶段MRG方法：首先训练一个分类器，用于检测输入图像是否存在任何异常；随后将分类后的图像分别输入两个基于transformer的生成器，即“疾病特异性生成器”和“无病生成器”，以生成对应的报告。为了提升对生成报告是否正确描述输入图像中出现的疾病的临床评估效果，我们提出了多样性敏感度（diverse sensitivity, DS）这一新指标，用于检查生成的疾病是否与真实标注匹配，并衡量所有生成疾病的多样性。结果表明，所提出的两阶段生成框架和增强策略使DS得到了显著提升，说明与表征不足的疾病相关的长尾问题得到了明显缓解。
英文摘要：
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases.
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