Chest radiology report generation based on cross-modal multi-scale feature fusion

Chest radiology imaging plays a crucial role in the early screening, diagnosis, and treatment of chest diseases. The accurate interpretation of radiological images and the automatic generation of radiology reports not only save the doctor's time but also mitigate the risk of errors in diagnosis. The core objective of automatic radiology report generation is to achieve precise mapping of visual features and lesion descriptions at multi-scale and finegrained levels. Existing methods typically combine global visual features and textual features to generate radiology reports. However, these approaches may ignore the key lesion areas and lack sensitivity to crucial lesion location information. Furthermore, achieving multi-scale characterization and fine-grained alignment of medical visual features and report text features proves challenging, leading to a reduction in the quality of radiology report generation. Addressing these issues, we propose a method for chest radiology report generation based on cross-modal multi-scale feature fusion. First, an auxiliary labeling module is designed to guide the model to focus on the lesion region of the radiological image. Second, a channel attention network is employed to enhance the characterization of location information and disease features. Finally, a cross-modal features fusion module is constructed by combining memory matrices, facilitating fine-grained alignment between multi-scale visual features and reporting text features on corresponding scales. The proposed method is experimentally evaluated on two publicly available radiological image datasets. The results demonstrate superior performance based on BLEU and ROUGE metrics compared to existing methods. Particularly, there are improvements of 4.8% in the ROUGE metric and 9.4% in the METEOR metric on the IU X-Ray dataset. Moreover, there is a 7.4% enhancement in BLEU-1 and a 7.6% improvement in the BLEU-2 on the MIMIC-CXR dataset.