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基于深度学习的乳腺病变分类,使用动态超声波视频

Guojia Zhao¹, Dezhuag Kong², Xiangli Xu³

¹Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China; Department of Ultrasound, Lin Yi People's Hospital, Linyi, Shandong, China.

European journal of radiology

|June 8, 2023

概括

使用乳房超声波动态视频的深度学习 (DL) 模型在分类乳房病变方面取得了比静态图像和放射科医生更高的准确性. 这种人工智能方法显示出改善乳腺癌诊断的前景.

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:

乳房超声波对于病变检测至关重要.
当前的诊断模型通常依赖于静态图像,可能缺少动态信息.
放射科医生的表现可能因经验和图像类型而异.

研究的目的:

使用乳房超声波动态视频开发深度学习 (DL) 分类模型.
为了比较DL视频模型对DL静态图像模型和人类放射科医生的诊断性能.

主要方法:

分析了888名患者的1000个乳腺病变.
两个DL模型 (DL-视频,DL-图像) 分别使用3D和2DResnet-50架构进行训练.
模型和六名放射科医生评估了一组病变的测试,比较静态图像和动态视频的性能.

主要成果:

与DL图像模型 (0.925) 和放射科医生 (0.779-0.912) 相比,DL视频模型在曲线下的面积 (0.969) 显著更高.
放射科医生用动态视频比静态图像的效果更好.
增加放射科医生的资历与改善两种图像类型的诊断性能相关.

结论:

DL视频模型有效地利用空间和时间信息来准确地分类乳腺损伤.
这种人工智能驱动的方法在诊断性能上超越了传统的DL模型和放射科医生.
临床实施DL视频模型可以提高乳腺癌诊断的准确性.

关键词:

乳腺癌乳腺癌乳腺癌深度学习是一种深度学习.诊断表现的表现诊断表现放射科医生放射科医生超声波动态视频超声波

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