bibtext: @InProceedings{ Liu_Structural_MICCAI2024,
                   author = { Liu, Kang and Ma, Zhuoqi and Kang, Xiaolu and Zhong, Zhusi and Jiao, Zhicheng and Baird, Grayson and Bai, Harrison and Miao, Qiguang },
                   title = { { Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: The automated generation of imaging reports proves invaluable in alleviating the workload of radiologists. A clinically applicable reports generation algorithm should demonstrate its effectiveness in producing reports that accurately describe radiology findings and attend to patient-specific indications. In this paper, we introduce a novel method, Structural Entities extraction and patient indications Incorporation (SEI) for chest X-ray report generation. Specifically, we employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports and improve the quality of factual entity sequences. This reduces the noise in the following cross-modal alignment module by aligning X-ray images with factual entity sequences in reports, thereby enhancing the precision of cross-modal alignment and further aiding the model in gradient-free retrieval of similar historical cases. Subsequently, we propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications. This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients. This, in turn, assists in triggering the text decoder to produce high-quality reports. Experiments conducted on MIMIC-CXR validate the superiority of SEI over state-of-the-art approaches on both natural language generation and clinical efficacy metrics. The code is available at https://github.com/mk-runner/SEI.
title: Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation
authors: Liu, Kang Ma, Zhuoqi Kang, Xiaolu Zhong, Zhusi Jiao, Zhicheng Baird, Grayson Bai, Harrison Miao, Qiguang