加强乳腺磁共振成像细分,采用联合的半监督方法
Bowen Zheng1, Jie Hou1, Zhiyuan Zheng2
1Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, China.
Scientific reports
|December 4, 2025
概括
联合的半监督学习可以使用有限的数据进行准确的乳房MRI细分. 这种保护隐私的方法增强了模型的概括性,并且在多个机构中优于现有的技术.
科学领域:
- 医疗成像中的人工智能
- 机器学习用于医疗保健
- 放射学和诊断成像 放射学和诊断成像
背景情况:
- 深度学习显著改善了乳腺磁共振成像 (MRI) 的自动细分,帮助诊断.
- 训练深度学习模型需要大量的注释数据,这对个体机构,特别是较小的机构来说是一个挑战.
- 数据注释是资源密集型,限制了医疗成像中强大的AI模型的开发.
研究的目的:
- 为自动化乳腺MRI细分开发一个联合的半监督学习框架.
- 为了最大限度地利用资源,并在合作机构中保护数据隐私.
- 用有限的注释数据来增强模型的稳定性和概括性.
主要方法:
- 实施了联合学习方法,每个机构都在当地培训模型.
- 利用半监督学习,对未注释的样本进行扰动,并为本地培训提供特征.
- 设计了对注释和未注释数据的联合优化损失函数.
主要成果:
- 在三家医院的乳房MRI细分方面取得了卓越的表现.
- 获得了94.8%的子相似度系数 (DSC) 和86.6%的欧盟交叉点 (IoU).
- 超过现有模型的性能高达8.8% (DSC) 和12% (IoU).
结论:
- 拟议的联合半监督学习框架有效地解决了乳腺MRI细分中的数据限制.
- 该方法提高了模型性能和概括性,同时保持了数据隐私.
- 这种方法为医疗机构开发先进的人工智能诊断工具提供了可扩展的解决方案.
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