用半监督深度学习与标记和未标记图像在光素血管学中定位多性冠状血管病变
Chung-Chien Lin1, Yu-Ning Tsao1, Wei-Yang Lin1
1Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan.
Translational vision science & technology
|October 14, 2025
概括
一种新型的深度学习模型有效地使用光素血管学 (FA) 图像对多冠状血管病 (PCV) 进行细分,减少了对广泛注释和绿色血管学的需求.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 聚冠状血管病变 (PCV) 是视力障碍的重要原因之一.
- 在光素血管学 (FA) 中精确细分PCV病变对于诊断和治疗监测至关重要.
- 传统的方法通常需要像素级的注释或额外的成像方式,如氨酸绿色血管学 (ICGA).
研究的目的:
- 开发一个具有成本效益的,半监督的深度学习模型,用于在FA图像中对PCV进行细分.
- 利用大量未标记的FA图像数据集来提高细分的准确性.
- 减少对像素级注释和PCV分析的ICGA的依赖.
主要方法:
- 开发了平均教师 (MT) 模型的一个变体,一个半监督的卷积神经网络.
- 该模型使用标记和未标记的FA图像进行训练.
- 两个相同的网络 (学生和教师) 用于积极的学习和一致性监管.
主要成果:
- MT模型实现了0.577 (验证) 和0.518 (测试) 的Dice相似系数.
- 豪斯多夫距离为37.28像素 (验证) 和43.05像素 (测试).
- 即使标记数据减少了一半,MT模型的表现也超过了AG-PCVNet,这是一个专门的反复卷积神经网络.
结论:
- 半监督深度学习有效地使用丰富的未标记的FA图像对PCV病变进行细分.
- 该模型消除了在PCV监测培训中需要ICGA的需求.
- 与完全监督模型相比,在高光的情况下观察到更好的准确性.
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