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从乳房超声波图像使用视觉和语言变压器生成自动化放射性报告

Shaheen Khatoon¹, Azhar Mahmood¹

¹Department of Computer Science and Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London E16 2RD, UK.

Journal of imaging

|February 26, 2026

概括

这项研究引入了一个新的AI系统,使用多式变压器自动生成乳房超声报告. 基于BioBERT的模型提高了临床准确性,而GPT-2提高了报告可读性.

科学领域:

人工智能的人工智能
医疗成像医学成像
自然语言处理自然语言处理.

背景情况:

乳房超声波对于检测异常至关重要,但报告生成是手动和主观的.
目前用于报告生成的AI经常使用较旧的架构,限制其理解复杂医疗细节的能力.
自动化系统可以提高放射性报告的效率和一致性.

研究的目的:

开发一种新型的多式变压器框架,用于自动生成乳房超声波报告.
来自超声波图像的视觉特征与语言模型的文本信息进行整合.
提高人工智能生成的放射学报告的准确性和流性.

主要方法:

使用视觉变压器 (ViT) 来从乳房超声波扫描中提取图像特征.
员工预先训练的语言模型 (BERT,BioBERT,GPT-2) 用于文字数据处理.
开发了一种多式变压器解码器,通过结合视觉和文本信息来生成报告.

主要成果:

与一般语言模型相比,基于BioBERT的模型显示出更高的临床特异性.
基于GPT-2的解码器显著提高了生成报告的语言流性.
多式联网方法有效地整合了视觉和文本数据,以生成全面的报告.

结论:

拟议的基于变压器的框架为自动化乳房超声波报告生成提供了一个有希望的方法.
整合BioBERT等特定领域的语言模型可以提高临床相关性.
该系统有可能简化放射学工作流程并提高报告质量.

关键词:

生物贝尔特 (BioBERT) 是一种生物贝尔特.在 GPT-2 中使用.自动报告生成自动报告生成多模式学习是多模式学习.视觉变压器视觉变压器

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