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通过金字塔视觉图形卷积网络对光学连贯断层扫描图像进行视网膜疾病的分类

Jin Qian¹, Lei Tao¹, Changhao Gong¹

¹Jiangsu Key Laboratory of Intelligent Medical Image Computing (IMIC), School of Artificial Intelligence, Nanjing University of Information Science and Technology, 210044 Nanjing, China.

Biomedical optics express

|July 18, 2025

概括

一个新的金字塔视觉图卷积网络 (PVGCN) 通过将图像区域视为连接节点,有效地将视网膜疾病分类到光学连贯断层扫描 (OCT) 图像中,其性能优于现有方法.

科学领域:

医学成像分析分析医学成像分析
深度学习用于眼科.

背景情况:

深度神经网络 (DNN) 广泛用于视网膜疾病分类,用于光学连贯性断层扫描 (OCT) 图像.
传统的DNN因其基于网格的处理而与OCT图像中的不规则结构作斗争,导致性能不佳.

研究的目的:

引入一种新的视觉神经网络模型,即金字塔视觉图卷积网络 (PVGCN),用于在OCT图像中改进视网膜疾病的分类.
解决传统DNN在处理复杂的视网膜结构方面的局限性.

主要方法:

PVGCN模型使用视觉图块将图像分割成节点,将其表示为图形数据,以获得更好的结构相关性.
采用金字塔结构来整合多层次特征,捕获层次信息并减少模型参数.
图形卷积和前网络用于模拟图像区域之间的关系并减轻过度平滑.

主要成果:

在两个独立的数据集上,PVGCN模型实现了0.9954和0.9787的高精度.
该模型与现有的视网膜疾病分类方法相比,显示出更高的性能.
发现PVGCN的诊断能力与人类专家的诊断能力相当.

结论:

拟议的PVGCN模型提供了一种灵活和有效的方法,用于从OCT图像中分类视网膜疾病.
基于图形和多尺度的功能集成克服了传统DNN的局限性,提高了诊断准确度.
这种新的架构对推进眼睛疾病的自动检测具有重大前景.

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