域适应性半监督学习,以有效地检测罕见的病理病变,并尽量减少注释
Isao Matsui1,2,3, Ayumi Matsumoto4, Atsuhiro Imai4
1Department of Nephrology, Graduate School of Medicine, The University of Osaka, Suita, Osaka, Japan. matsui@kid.med.osaka-u.ac.jp.
NPJ digital medicine
|November 23, 2025
概括
对于罕见的病变检测,人工智能 (AI) 在有限的专家数据和多种类型的扫描仪上扎. 我们的新方法结合了域名适应和半监督学习,以提高不同医院和扫描仪的AI准确性.
科学领域:
- 医疗成像医学成像
- 人工智能在病理学中的应用
- 计算病理学计算病理学
背景情况:
- 对罕见病变病变检测的AI面临挑战,原因是专家注释稀缺,机构间的领域转移.
- 性能下降显著,罕见的病变的检测精度降低了高达70.3%.
研究的目的:
- 开发和评估一种强大的AI方法,用于在多机构脏活检中检测罕见的病理病变.
- 解决由不同类型的扫描仪和机构变化引起的领域转移问题.
主要方法:
- 利用来自22家医院的多机构脏活检数据,使用三种扫描仪类型 (NDPI,VSI,SVS).
- 集成的半监督学习与剩余的基于CycleGAN的域调整.
- 评估了不同场景的取决于背景的最佳策略.
主要成果:
- 从 55.9 降低了机构之间的平均 Fréchet 开始距离,从 20.2 降低到 55.9,保持了诊断形态.
- 半监督学习在同一医院的情景中,提高了罕见病变检测率15.2-17.7%.
- 组合GAN-半监督方法在交叉扫描器场景 (NDPI与VSI) 中提高了半月亮的检测率高达63.4%.
结论:
- 拟议的方法允许强大的AI性能用于在各种医疗保健环境中检测罕见的病理病变.
- 这种方法最大限度地减少了对广泛的专家注释的需求,同时提高了模型的通用性.
- 特定于环境的策略优化AI性能,取决于数据的变化 (机构内部与机构间).
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