Automated radiology reporting holds immense clinical potential in alleviating the burdensome workload of radiologists and mitigating diagnostic bias. Recently, retrieval-based report generation methods have garnered increasing attention due to their inherent advantages in terms of the quality and consistency of generated reports. However, due to the long-tail distribution of the training data, these models tend to learn frequently occurring sentences and topics, overlooking the rare topics. Regrettably, in many cases, the descriptions of rare topics often indicate critical findings that should be mentioned in the report. To address this problem, we introduce a Topicwise Separable Sentence Retrieval (Teaser) for medical report generation. To ensure comprehensive learning of both common and rare topics, we categorize queries into common and rare types to learn differentiated topics, and then propose Topic Contrastive Loss to effectively align topics and queries in the latent space. Moreover, we integrate an Abstractor module following the extraction of visual features, which aids the topic decoder in gaining a deeper understanding of the visual observational intent. Experiments on the MIMIC-CXR and IU X-ray datasets demonstrate that Teaser surpasses state-of-the-art models, while also validating its capability to effectively represent rare topics and establish more dependable correspondences between queries and topics.

我们介绍了一种针对医学报告生成的主题分离句子检索（Teaser）方法，通过将查询分类为常见和少见类型以学习不同的主题，提出主题对比损失来有效地对齐主题和查询，同时使用抽象器模块在提取视觉特征后帮助主题解码器更深入地理解视觉观察的意图，在MIMIC-CXR和IU X-ray数据集上的实验证明了Teaser超过了现有的模型，同时验证了其有效地表示罕见主题并建立查询和主题之间更可靠的对应关系的能力。

针对医疗报告生成的主题可分离句子检索