一个基于领域知识的可解释的深度学习系统,用于改善临床乳腺超声波诊断
Lin Yan1, Zhiying Liang2, Hao Zhang3
1School of Mathematics, Xi'an University of Finance and Economics, Xi'an, China.
Communications medicine
|May 17, 2024
概括
一个新的可解释的深度学习系统可以准确地从多式超声波图像中预测乳腺癌风险,帮助放射科医生并改善乳腺癌查的临床结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 深度学习在解释乳房超声波图像方面表现有前途.
- 深度学习的"黑子"性质阻碍了临床整合和放射科医生信任.
研究的目的:
- 开发基于领域知识的可解释深度学习系统,用于使用多式超声波图像进行乳腺癌风险预测.
- 加强人机协作,以提高诊断准确度.
主要方法:
- 一个深度学习系统被训练在4320多模式乳房超声波图像从1440个活检确认的病变.
- 从两个医院的1348名患者的数据被前性地收集并分为培训,验证和测试队列.
- 该系统将域名知识纳入了可解释性.
主要成果:
- 可解释的深度学习系统实现了0.902的ROC曲线下的面积,相当于经验丰富的放射科医生.
- 在测试队列中,灵敏度为75.2%,特异性为91.8%.
- 初级放射科医生表现出改善的结果,而高级放射科医生在使用该系统时报告了增加的信心.
结论:
- 开发的可解释深度学习系统证明了乳腺癌查的临床适用性.
- 它可以支持辅助或二读工作流程,提高放射科医生的性能和信心.
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