  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.
