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相关概念视频

Diabetic Retinopathy01:27

Diabetic Retinopathy

55
DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
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Diabetic Nephropathy01:28

Diabetic Nephropathy

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Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration...
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相关实验视频

Updated: May 5, 2026

An Ex Vivo Tissue Culture Model for Fibrovascular Complications in Proliferative Diabetic Retinopathy
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高效的基于同型加密的联合学习,用于糖尿病视网膜病变的分类.

Christopher Nielsen1,2, Matthias Wilms1,3,4,5,6, Nils D Forkert1,3,4,7

  • 1University of Calgary, Department of Radiology, Calgary, Alberta, Canada.

Journal of medical imaging (Bellingham, Wash.)
|June 4, 2025
PubMed
概括

这项研究引入了一个保护隐私的联合学习 (FL) 框架,用于糖尿病视网膜病变 (DR) 诊断. 这种新的方法提高了模型的概括性,同时保护敏感的患者数据免受泄露.

关键词:
糖尿病视网膜病变 糖尿病视网膜病变联合学习的联合学习基金斯成像技术公司同型型的加密方式.

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科学领域:

  • 眼科医生 眼科 眼科
  • 计算机科学 计算机科学
  • 医疗成像医学成像

背景情况:

  • 糖尿病视网膜病变 (DR) 是全球劳动年龄成人失明的主要原因.
  • 机器学习 (ML) 对DR诊断具有前景,但需要多样化的数据来实现概括性.
  • 联合学习 (FL) 能够实现分散的培训,但面临着隐私问题,如梯度反转攻击.

研究的目的:

  • 开发和测试一个计算高效的FL框架,集成同型加密 (HE) 以保护DR选中的隐私.
  • 在分散的ML模型培训期间,保护患者数据免受隐私侵犯.

主要方法:

  • 利用了来自APTOS-2019和ODIR-5K的6457张视网膜底部图像的数据集.
  • 使用RETFound,一个预训练的基础模型,用于从分布式视网膜图像中提取特征.
  • 训练了一种轻量级的多类后勤回归头 (MLRH) 模型,使用具有加密功能的FL和HE进行隐私.

主要成果:

  • 经过FL训练的MLRH模型实现了与集中数据上完全微调模型 (AUC为0.93和0.78) 相比的性能.
  • 显著的效率提升:计算时间减少了95.9倍,数据传输减少了63.0倍.
  • 证实集成的HE有效地保护了患者数据免受梯度逆转攻击.

结论:

  • 基于ML的高级隐私保护DR查技术.
  • 该框架支持公平的视力护理,通过使强大和安全的DR诊断成为可能.
  • 突出结合FL和HE的潜力,以确保医疗数据的安全分析.