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可解释的集体学习方法用于OCT检测与转移学习

Jiasheng Yang¹, Guanfang Wang^2,3, Xu Xiao⁴

¹Academician Workstation, Changsha Medical University, Changsha, Hunan, China.

|March 22, 2024

概括

本研究介绍了一种使用转移学习进行光学连贯断层扫描 (OCT) 图像分析的AI方法. 可解释组合模型在检测与年龄相关的黄斑变性和糖尿病黄斑瘤方面实现了100%的准确性.

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:

在光学连贯断层扫描 (OCT) 图像检测中的人工智能 (AI) 对于减少临床医生的工作量和提高诊断准确性至关重要.
可解释性和准确性是推动AI在临床工作流程中的关键,特别是在视网膜成像中.

研究的目的:

开发和评估一种可解释的整体AI方法,用于在OCT图像中检测 fundus 疾病.
评估转移学习和预训练权重对基于OCT的疾病检测AI模型性能的影响.

主要方法:

利用公开的OCT数据集与正常受试者,干燥的与年龄相关的黄斑变性 (AMD) 和糖尿病黄斑 (DME) 样本.
员工通过预先训练的ImageNet权重转移学习,通过多数软民意调查比较个人网络性能,然后通过多数软民意调查进行组合.
使用Grad-CAM和CAM可解释性来可视化学习的特征.

主要成果:

预训练的ImageNet权重显著提高了个体网络性能,从68.17%提高到92.89%.
整体模型在区分AMD,DME和正常受试者方面实现了100%的准确性.
通过Grad-CAM可视化显示了病变区域的准确识别.

结论:

提出的可解释组合人工智能方法证明了视网膜OCT图像检测的高准确性和可解释性.
转移学习和组合方法是提高AI在诊断视网膜疾病中的性能的有效策略.
这种方法显示了简化眼科临床工作流程的潜力.

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