多阶段的半监督学习增强了白质的高强度细分
Kauê T N Duarte1,2, Abhijot S Sidhu3,4, Murilo C Barros5
1Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
Frontiers in computational neuroscience
|November 6, 2024
概括
一种新的多阶段半监督学习 (M3SL) 方法有效地使用有限的注释数据对白质超强度 (WMHs) 进行细分. 这种方法提高了跨不同数据集和临床条件的概括性,优于传统和转移学习技术.
科学领域:
- 医疗成像和人工智能的人工智能
- 神经科学和神经学是神经科学和神经学.
- 机器学习用于医疗保健
背景情况:
- 白质超强度 (WMHs) 在老年人中很常见,并与痴呆和中风风险增加有关.
- 手动对WMH进行细分是耗时和劳动密集的,阻碍了创建大型注释数据集.
- 有限的注释数据对开发用于WMH细分的监督机器学习模型提出了重大挑战.
研究的目的:
- 开发和评估一个多阶段的半监督学习 (M3SL) 方法,用于自动化白质超强度 (WMH) 细分.
- 为了应对WMH细分中的有限注释数据的挑战,使用未注释和黄金标准注释数据的组合.
- 改进WMH细分模型在不同MRI扫描仪供应商和临床人群 (认知正常,轻度认知障碍,阿尔茨海默病) 中的概括性.
主要方法:
- 实施了多阶段的半监督学习 (M3SL) 框架,将未注释数据的传统处理方法 ("铜"和"银") 与一小部分"黄金"标准注释相结合.
- 使用M3SL方法在U-Net架构中微调模型重量,用于WMH细分.
- 通过使用来自三个供应商的多个扫描仪的数据和各种临床队列 (认知正常,MCI,AD) 的数据进行了模型的训练和验证.
主要成果:
- 与传统和转移学习深度学习方法相比,M3SL方法在不同扫描仪供应商和临床阶段 (CN,MCI,AD) 展示了优越的概括性能.
- 使用M3SL观察到WMH细分精度的显著改善,用F-测量,IoU和豪斯多夫距离 (p < 0.001) 来测量.
- 该研究证实了在多阶段学习框架内使用自动化,非机器学习工具的实用性,以提高模型性能.
结论:
- M3SL方法有效地克服了WMH分割的稀缺注释数据的局限性.
- 这种方法提高了模型性能和概括性,为临床研究中大规模WMH分析提供了可行的解决方案.
- 自动化工具在半监督学习框架中发挥着至关重要的作用,提高了医疗图像细分的效率和准确性.
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