A chest imaging diagnosis report generation method based on dual-channel transmodal memory network

Zilong Dong, Jing Lian, Xiangzi Zhang, Bingtao Zhang, Jizhao Liu, Jiajun Zhang, Huaikun Zhang

Abstract: Medical imaging is critical for clinical diagnosis. In the past few years, medical imaging data has been steadily growing at an annual rate of 30%, while the annual growth rate in the number of radiologists is only 4. 1%. The traditional manual methods of chest imaging reports result in high information load and workload with low relevance of images and texts. Here we propose a chest CT imaging diagnosis report generation network to effectively integrate image and text feature information, improve the relevance of the cross-modal networks and automatically generate imaging diagnosis reports. The network includes three major modules: a novel ResNetII for information extraction, an image–text dual-channel transmodal memory network (DCTMN), and a dual-channel decoder module combined with LSTM gate. We validate the effectiveness of the proposed network on the IU X-ray and the MIMIC-CXR datasets. Our analysis indicates that the proposed method can strengthen the relationship between chest imaging and text information, ultimately achieving the automatic generation of chest imaging diagnosis reports.