Topicwise Separable Sentence Retrieval for Medical Report Generation
This addresses a critical issue in automated radiology reporting for clinicians, where rare findings are often missed, though it is an incremental improvement over existing retrieval-based methods.
The paper tackles the problem of retrieval-based medical report generation models overlooking rare but critical topics due to long-tail data distribution, and introduces Teaser, which improves performance by categorizing queries and using topic contrastive loss, achieving state-of-the-art results on MIMIC-CXR and IU X-ray datasets.
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.