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双域全球变压器网络,用于一般化玻璃眼病阶段分类

Dipankar Das, Deepak Ranjan Nayak, Ram Bilas Pachori

IEEE journal of biomedical and health informatics

|May 29, 2025

概括

一个新的Glaucoformer网络通过分析空间和频率领域的 fundus 图像来有效地分类青光眼的阶段. 这种双域方法捕捉了本地和全球特征,以提高诊断准确度.

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:

从 fundus 图像来分类青光眼的阶段是具有挑战性的,因为细微的变化和阶段间的相似性.
现有的卷积神经网络 (CNN) 捕捉了局部特征,但错过了全球依赖.
视觉转换器模拟全球环境,但忽视了当地特征和频率域分析.

研究的目的:

开发一个新的双域全球变压器网络,Glaucoformer,用于准确的青光眼病阶段分类.
通过整合空间和频率域分析来增强特征表示.

主要方法:

引入了一种双域全球变压器层 (DGTL),具有双域通道和空间注意力.
集成了一个里埃域特征分析器 (FDFA) 来处理频域信息.
利用可变形卷曲来处理复杂的病变不规则,并使用共享的关键查询方案以提高效率.

主要成果:

与现有的CNN和视觉转换器模型相比,Glaucoformer在分类青光眼的阶段方面表现出更高的有效性.
双域方法成功地利用了空间和频率领域的本地和全球上下文特征.
该模型在未见的数据集上显示出强大的概括性.

结论:

拟议的Glaucoformer网络提供了一个强大的新工具,用于对格洛科马的阶段分类.
整合双域分析显著提高了模型学习歧视性特征表示的能力.
这种方法对推进自动化青光眼诊断具有前景.

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