Radiology report generation aims to provide comprehensive clinical descriptions and ease radiologists' workloads. Previous research has explored using knowledge graphs and auxiliary classification tasks to enhance the model's ability to generate accurate reports. However, due to the lack of information in the knowledge graphs or insufficient class label information, these methods fail to provide models with clinical severity information about the same disease at different stages of development, resulting in less accurate reports. To address this issue, we propose a Severity-Guided Radiology Report Generation method (SR2Gen), which guides the model in identifying internal severity variations of the disease from both explicit and implicit dimensions. Specifically, SR2Gen includes two innovative modules: a Knowledge Enhancement Module (KEM) and a Disease SeverityAware Module (DSAM). First, KEM explicitly guides the report generation model by constructing a knowledge graph containing disease severity information as prior knowledge. Secondly, DSAM enhances the severity-aware classifier using pseudo-labels generated through momentum distillation and further incorporates an adaptive disease severity learning method, implicitly guiding the model to learn disease progression. Extensive experiments and analyses on IU X-Ray and MIMIC-CXR datasets demonstrate that SR2Gen outperforms previous state-of-the-art methods.
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