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基于OCT的疾病诊断的全面调查:从特征提取方法到强大的安全框架

Alex Liew¹, Sos Agaian²

¹Graduate Center, City University of New York, New York, NY 10016, USA.

Bioengineering (Basel, Switzerland)

|September 27, 2025

概括

本综述比较了光学连贯断层扫描 (OCT) 特性提取方法用于诊断眼睛疾病. 它强调了深度学习和手工制作的技术,评估了它们的性能和对抗对抗攻击的安全性.

科学领域:

眼科和医学成像学
医疗保健中的人工智能
计算机视觉用于诊断.

背景情况:

光学连贯断层扫描 (OCT) 对于诊断视网膜疾病至关重要,如黄斑变性和糖尿病视网膜病变.
早期检测和监测视神经头部的变化对于诸如玻璃眼等疾病至关重要.
缺乏对基于OCT的特征提取技术进行疾病预测的比较分析.

研究的目的:

调查和比较分析手工制作和深度学习特征提取方法用于使用OCT预测眼病.
根据精度,灵敏度,特异性和计算成本来评估不同方法的性能.
探索对海外国家和地区诊断系统的对抗性攻击,并提出强有力的防御策略.

主要方法:

在过去的20年中,基于OCT的眼病预测技术的综合文献综述.
使用准确度,灵敏度和特异性等指标对方法的评估.
评估公共OCT数据集和分析对抗性攻击的漏洞.

主要成果:

确定了关键疾病,包括玻璃眼瘤,糖尿病视网膜病变,白内障,眼和黄斑变性.
对比了各种特征提取方法的有效性和计算需求.
证明了海外国家和地区的诊断系统对敌对攻击的脆弱性,并提出了有效的防御策略.

结论:

将经典和深度学习方法与强大的防御相结合,提高了OCT诊断的安全性和可靠性.
拟议的国防战略提供了实际的整合和卓越的性能.
这些发现为未来的研究和基于OCT的诊断工具的临床实施提供了指导.

关键词:

敌对的攻击是对抗性的攻击.临床决策支持系统临床决策支持系统深度学习模型的深度学习模型糖尿病视网膜病变糖尿病视网膜病变青光眼的检测仪青光眼检测仪手工制作的特点是手工制作的特点.光学连贯性断层扫描 (OCT)在医学成像中的强度.人工智能模型中的安全性

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