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对于糖尿病视网膜病变的深度学习概括,从 fundus 图像进行糖尿病视网膜病变的分期

Yevgeniy Men^1,2, Jonathan Fhima^2,3, Leo Anthony Celi^4,5,6

¹Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion, Israel Institute of Technology, Haifa 3200003, Israel.

Physiological measurement

|January 9, 2025

概括

一个新的深度学习模型,DRStageNet,从 fundus 图像准确地分阶段糖尿病视网膜病变 (DR),克服了跨不同数据集的概括挑战,以改进视力损失的检测.

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:

糖尿病视网膜病变 (DR) 是导致视力丧失的主要原因,需要早期检测.
目前用于从基底图像进行DR分期的AI模型通常由于域移动而无法在多种数据集中概括.

研究的目的:

开发和验证DRStageNet,这是一个用于强大的DR分期的深度学习模型.
为了解决现有的DR检测算法的概括限制.

主要方法:

使用六个公共数据集开发了DRStageNet,其中包括91,984张数字基金图像 (DFI).
基准测试了五个自我监督的视觉变换器 (ViT),选择了DINOv2进行进一步培训.
采用多源域 (MSD) 微调策略来增强模型通用化.

主要成果:

与其他ViT相比,DINOv2实现了27.4%的L-Kappa改进.
在五个目标领域中,MSD微调在四个目标领域中提高了性能.
DRStageNet正确地分类了77.5%的错误标记的图像,这表明其准确度很高.

结论:

DRStageNet展示了精确的糖尿病视网膜病变阶段,有效地解决跨领域概括问题.
开发的模型及其可解释性热图为临床应用提供了一个有前途的工具.

关键词:

深度学习是一种深度学习.糖尿病视网膜病变糖尿病视网膜病变图片来源: 基金图像基金自主监督学习学习变压器变压器变压器

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