简化放射科医生对体积医学图像的注释过程,使用少数镜头学习
Alina Ryabtsev1, Richard Lederman2, Jacob Sosna2
1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
概括
这项研究引入了一种新的方法,以减少放射科医生对医学成像中的深度学习的注释努力. 这种方法显著缩短了病变检测和轮的校正时间,使人工智能模型能够更好地训练.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 手动放射科医生注释是训练体积医学成像中的深度学习模型的瓶.
- 少数射击学习模型与小结构 (如病变) 斗争,尽管与较大的结构出色.
研究的目的:
- 开发一种新的方法,将少量拍摄和完全监督的学习相结合,以降低手动注释成本.
- 提高创建注释数据集的效率,用于医学成像中的深度学习.
主要方法:
- 一种使用小标记数据集和大无标记数据集的混合方法.
- 利用少数射击学习 (UniverSeg) 预先注释扫描,放射科医生纠正一个子集.
- 优化支持集和优先级扫描需要最小的纠正,以实现代的改进.
主要成果:
- 在CT和MRI扫描中对肝脏,肺部和大脑病变进行验证 (375次扫描,5933次病变).
- 实现了显著的纠正力度降低:错过的病变减少了34%,错误的病变减少了387%,轮纠正减少了130%,要纠正的像素减少了424%.
- 与从头开始手动注释相比,证明了显著的效率提升.
结论:
- 该方法有效地减少了放射科医生对小型结构的注释工作量.
- 能够创建高质量的注释数据集,适合训练深度学习模型.
- 这种方法是多功能和适用于不同的器官,病变类型和成像模式.
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