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The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
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相关实验视频

Updated: May 28, 2025

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
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深度失衡回归模型用于预测视网膜照片中的折射误差.

Samantha Min Er Yew1,2, Xiaofeng Lei3, Yibing Chen4

  • 1Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Ophthalmology science
|February 11, 2025
PubMed
概括

深度失衡回归集成深度学习模型准确地预测视网膜图像的折射误差. 这种方法解决了数据不平衡,并显示了机会性眼睛查的前景.

关键词:
深度学习是一种深度学习.不平衡回归是一种不平衡的回归.折射误差是指一个折射误差.视网膜照片 视网膜照片

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 深度学习 (DL) 模型显示出预测眼镜图像的折射误差的前景.
  • 现有的研究往往忽视了数据集不平衡和外部验证,限制了现实世界的适用性.

研究的目的:

  • 将深度不平衡回归 (DIR) 集成到ResNet和视觉变压器模型中,用于折射误差预测.
  • 为了解决不平衡数据集的偏差,并使用视网膜照片提高预测准确性.

主要方法:

  • 开发并比较了带有和没有DIR集成的ResNet34和SwinV2 (视觉变压器) 模型.
  • 包含标签分布平滑和特征分布平滑技术.
  • 在内部数据集 (新加坡眼病流行病学研究,英国生物库) 和外部数据集 (新加坡前性研究,北京眼睛研究) 上验证的模型.

主要成果:

  • 在预测球体和球体等效 (SE) 功率方面,DIR集成模型 (ResNet34-DIR,SwinV2-DIR) 的表现明显优于基线模型.
  • 通过DIR模型实现了较低的平均绝对误差 (MAE):球形功率为0.84D (ResNet34-DIR) 和0.77D (SwinV2-DIR);SE功率为0.78D (ResNet34-DIR) 和0.75D (SwinV2-DIR).
  • 在内部和外部测试数据集中观察到一致的性能改进.

结论:

  • 深度失衡回归有效地减轻折射误差预测模型中的数据失衡.
  • 与DIR集成的DL模型显示了使用视网膜图像准确预测折射误差的巨大潜力.
  • 这种方法促进了对折射误差的机会性查,特别是在现有视网膜成像技术的环境中.