GMoD: Graph-Driven Momentum Distillation Framework with Active Perception of Disease Severity for Radiology Report Generation

Automatic radiology report generation is a challenging task that seeks to produce comprehensive and semantically consistent detailed descriptions from radiography (e.g., X-ray), alleviating the heavy workload of radiologists. Previous work explored the introduction of diagnostic information through multi-label classification. However, such methods can only provide a binary positive or negative classification result, leading to the omission of critical information regarding disease severity. We propose a Graph-driven Momentum Distillation (GMoD) approach to guide the model in actively perceiving the apparent disease severity implicitly conveyed in each radiograph. The proposed GMoD introduces two novel modules: Graph-based Topic Classifier (GTC) and Momentum Topic-Signal Distiller (MTD). Specifically, GTC combines symptoms and lung diseases to build topic maps and focuses on potential connections between them. MTD constrains the GTC to focus on the confidence of each disease being negative or positive by constructing pseudo labels, and then uses the multi-label classification results to assist the model in perceiving joint features to generate a more accurate report. Extensive experiments and analyses on IU-Xray and MIMIC-CXR benchmark datasets demonstrate that our GMoD outperforms state-of-the-art method. Our code is available at https://github.com/xzp9999/GMoD-mian .