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。