结合对比学习和特征差异自动生成医疗报告

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

自动生成医疗报告是一项具有挑战性的任务,因为它需要准确捕捉和描述异常区域,尤其是患者与正常人之间的差异。在大多数情况下,正常区域的描述在整个医疗报告中占主导地位,而现有的方法可能会由于数据偏差而无法关注异常区域。通过将对比学习与特征差异相结合,可以自动生成医疗报告,从而有效捕捉和描述异常区域。通过捕捉输入图像与正常图像之间的差异属性,该方法可以提供更准确的诊断报告,并更好地表现异常区域的视觉特征。具体来说,我们一方面提出了特征差异方法,使模型更加关注异常区域;另一方面,我们在特征差异方法的基础上,提出了结合对比学习来增强特征差异的视觉表示,从而提高模型的性能。在 IU-Xray 和 MIMIC-CXR 数据集上的实验结果证明了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic medical report generation combining contrastive learning and feature difference
The automatic medical report generation is a challenging task because it requires accurate capture and description of abnormal regions, especially for those discrepancies between patient and normal. In most cases, normal region descriptions dominate the entire medical report, and existing methods may fail to focus on abnormal regions due to data bias. Medical reports can be automatically generated by combining contrastive learning with feature difference in order to capture and describe abnormal regions effectively. By capturing discrepancy attributes between the input image and normal images, this method can provide more accurate diagnostic reports and better represent the visual features of abnormal regions. Specifically, we propose the feature difference approach to make the model focus more on abnormal regions, and on the other hand, we propose the combination of contrastive learning for enhancing the visual representation of feature difference based on the feature difference approach, thus improving the performance of the model. Experimental results on the IU-Xray and MIMIC-CXR datasets demonstrate the effectiveness of our approach.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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