一个分层的深度学习架构用于诊断视网膜疾病使用交叉模式的OCT到基底翻译在缺乏配对数据的情况下
Ekaterina A Lopukhova1, Gulnaz M Idrisova2, Timur R Mukhamadeev2
1Research Laboratory "Sensor Systems Based on Integrated Photonics Devices", Ufa University of Science and Technology, 32 Z. Validi Street, 450076 Ufa, Russia.
Journal of imaging
|January 27, 2026
概括
这项研究引入了用于自动视网膜疾病诊断的深度学习系统,改善了与年龄相关的黄斑退化 (AMD) 和糖尿病视网膜病变 (DR) 的检测. 该系统有效地处理复杂的案件和有限的数据,只使用OCT扫描才能实现可靠的分期.
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:
- 视网膜疾病的自动诊断,如与年龄相关的黄斑退化 (AMD) 和糖尿病视网膜病变 (DR) 是至关重要的.
- 挑战包括疾病并发症,阶级不平衡以及缺乏配对的OCT和基金数据.
研究的目的:
- 开发一个强大的深度学习系统,用于对视网膜疾病的多标签OCT查.
- 为了使糖尿病视网膜病变 (DR) 的分期仅使用OCT数据,当 fundus 图像不可用时.
- 为了解决疾病并发症和数据限制在自动视网膜疾病诊断.
主要方法:
- 一个分层的模块化深度学习系统,具有条件路由,用于多标签的OCT查.
- 使用隐藏桥梁进行跨模式的对齐,以将OCT嵌入到DR分期的基础功能空间中.
- 校准每类值和质量控制,以提高临床可靠性和仅用于OCT的DR分期.
主要成果:
- 在值校准后,实现了强大的多标签性能,宏F1得分为0.989±0.006.
- 证明可靠的校准,预期校准误差 (ECE) 为2.1±0.4%.
- 在96.1%的符合质量控制标准的病例中,仅对海外国家和地区的DR分期是可行的.
结论:
- 拟议的深度学习系统有效地解决了视网膜疾病自动诊断方面的挑战.
- 该系统允许使用OCT数据进行可靠的多标签查和DR分期,即使使用有限或未配对的 fundus 图像.
- 该方法通过在现实场景中提高诊断准确性和可行性来提高临床实用性.
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