Retrieval from Dynamic Phrases: Generating Radiograph Reports with Phrase-Level Template and Dynamic Memory Bank

Most retrieval-based report generation methods rely on sentence-level templates, which often introduce ambiguities due to similar semantics across different sentences. To overcome this, we propose a phrase-level framework, comprising automatic phrase template extraction and report generation based on retrieval. In the first stage, we introduce a phrase scoring mechanism to evaluate the semantics and importance of phrases, enabling efficient template extraction. In the second stage, we retrieve relevant templates and fuse their features with visual features from the radiograph through a Retrieval-Aggregation strategy. The dynamic update of the template bank during training improves template representations. Experiments on IU X-Ray and MIMIC-CXR datasets demonstrate the effectiveness of our method in generating accurate radiology reports.