了解胸部X射线扫描的转移学习与修改过的变压器架构的临床报告生成
Edward Vendrow1, Ethan Schonfeld2
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 50 Vassar St, Cambridge, MA, United States of America.
Heliyon
|July 31, 2023
概括
特定领域的预训练显著改善了胸部X射线的医学图像标题,优于罕见疾病的一般预训练. 综合方法提高了特定发现的准确性和整体绩效.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 自然语言处理自然语言处理.
背景情况:
- 从胸部X射线图生成临床报告至关重要,但耗时且容易出错.
- 自动化系统有望提高标准化,减少错误,提高医疗可访问性.
研究的目的:
- 为了证明医疗图像标题领域特定预培训的重要性.
- 提出和评估修改的变压器架构,用于从胸部X射线图中生成临床报告.
主要方法:
- 训练了修改后的网状记忆增强型变压器架构,采用了不同的预训练策略 (ImageNet,CheXpert和组合).
- 使用胸部放射图像作为生成临床报告的输入.
- 使用BLEU,ROUGE-L,CIDEr和临床CheXbert F1分数进行评估的模型.
主要成果:
- 图像网的预先培训不足以用于医学图像标题,特别是对于不太常见的疾病.
- 结合ImageNet和CheXpert预训练的双特征模型改善了特定条件 (胀,巩固,肺胸,支持设备) 和整体CheXbert F1得分的性能.
- 通过最先进的模型获得了竞争性分数.
结论:
- 针对特定领域的预培训对于有效的医学图像标题是必不可少的.
- 拟议的双重功能模型显示了改善自动化临床报告生成的希望,并保证了进一步的发展.
- 这种方法可以广泛应用于各种医疗图像标题任务.
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