Visual prior-based cross-modal alignment network for radiology report generation

计算机科学 人工智能 情态动词 水准点(测量) 工作量 过程(计算) 医学诊断 医学影像学 可视化 模式识别(心理学) 机器学习 计算机视觉 放射科 医学 操作系统 化学 高分子化学 地理 大地测量学
作者
Sheng Zhang,Chuan Zhou,Leiting Chen,Z.M. Li,Yuan Gao,Yongqi Chen
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:166: 107522-107522 被引量:3
标识
DOI:10.1016/j.compbiomed.2023.107522
摘要

Automated radiology report generation is gaining popularity as a means to alleviate the workload of radiologists and prevent misdiagnosis and missed diagnoses. By imitating the working patterns of radiologists, previous report generation approaches have achieved remarkable performance. However, these approaches suffer from two significant problems: (1) lack of visual prior: medical observations in radiology images are interdependent and exhibit certain patterns, and lack of such visual prior can result in reduced accuracy in identifying abnormal regions; (2) lack of alignment between images and texts: the absence of annotations and alignments for regions of interest in the radiology images and reports can lead to inconsistent visual and textual features of the abnormal regions generated by the model. To address these issues, we propose a Visual Prior-based Cross-modal Alignment Network for radiology report generation. First, we propose a novel Contrastive Attention that compares input image with normal images to extract difference information, namely visual prior, which helps to identify abnormalities quickly. Then, to facilitate the alignment of images and texts, we propose a Cross-modal Alignment Network that leverages the cross-modal matrix initialized by the features generated by pre-trained models, to compute cross-modal responses for visual and textual features. Finally, a Visual Prior-guided Multi-Head Attention is proposed to incorporate the visual prior into the generation process. The extensive experimental results on two benchmark datasets, IU-Xray and MIMIC-CXR, illustrate that our proposed model outperforms the state-of-the-art models over almost all metrics, achieving BLEU-4 scores of 0.188 and 0.116 and CIDEr scores of 0.409 and 0.240, respectively.
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