一个基于转移学习的多模式神经网络,将元数据和多种医疗图像结合起来,用于对青光眼的类型诊断
1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China. yilineuise@163.com.
Scientific reports
|July 26, 2023
概括
一个新的多式联络数据集和深度学习网络 (GMNNnet) 已被开发用于青光眼的诊断. 这种方法通过整合不同的数据类型来提高准确性,克服了这种视觉神经病的单模方法的局限性.
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:
- 玻璃眼是一种获得的视神经病变,导致不可逆转的视力丧失.
- 深度学习 (DL) 在医学图像识别方面表现有前途,但注释数据集很少.
- 由于疾病的复杂性,对青光眼诊断的单模方法往往不可靠.
研究的目的:
- 构建一种新的多式联络数据集,用于眼.
- 提出一个多式神经网络 (GMNNnet) 用于青光眼的诊断和分类.
- 为了应对数据注释和诊断准确性的挑战.
主要方法:
- 创建了一个多式数据集,包含了眼标签,电子医疗记录和四种图像类型.
- 设计了一个三分支神经网络 (GMNNnet),处理元数据,细分功能和医疗图像.
- 梯度加权类激活映射 (Grad-GAM) 和转移学习用于可解释性和数据稀缺性.
主要成果:
- 拟议的GMNNnet有效地整合了来自多式联络数据的高维玻璃眼信息.
- 该模型实现了出色的诊断和分类性能.
- 可解释性方法增强了对模型决策过程的理解.
结论:
- 开发的多式联络数据集和GMNNnet提供了一个强大的解决方案,用于青光眼的诊断.
- 整合不同的数据源可以显著提高诊断准确度.
- 这种方法推进了人工智能在眼科中的应用,用于治疗像绿眼病这样的复杂疾病.
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