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在糖尿病视网膜病症分级中解释深度神经网络:与人类决策标准的比较
Sangeeta Biswas1, Md Ahanaf Arif Khan1, Md Hasnain Ali1
1Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
Life (Basel, Switzerland)
|September 27, 2025
概括
这项研究调查了基于深度神经网络的自动糖尿病视网膜病变分类器 (ADRC) 是否使用与人类专家相同的标准. 使用可解释的人工智能,我们分析了ADRC对基金图像的决定,以确保可靠的DR分级.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 糖尿病视网膜病变 (DR) 是全球视力损失的主要原因.
- 使用深度神经网络 (DNN) 的自动糖尿病视网膜病变分类器 (ADRC) 显示出广泛查的前景.
- 基于DNN的ADRC的可靠性受到质疑,因为它们与人类视网膜专业人员 (HRPs) 的分级标准保持一致.
研究的目的:
- 调查基于DNN的ADRC是否使用与HRP相同的诊断标准来对DR严重程度进行分级.
- 通过将其决策过程与人类专家评估进行比较,评估ADRC的可靠性.
主要方法:
- 在公开可用的基金图像数据集上进行实验.
- 基于移动网络的ADRC的开发和使用.
- 应用两个可解释的人工智能 (XAI) 技术:梯度加权类激活图 (Grad-CAM) 和集成梯度 (IG) 来分析ADRC输出.
主要成果:
- 分析显示,与HRP相比,基于DNN的ADRC使用的标准存在差异.
- XAI技术提供了对影响ADRC决策的视觉特征的见解.
- 该研究强调了当前ADRCs临床可靠性的潜在问题.
结论:
- 基于DNN的ADRC可能不会始终使用与DR分级的人类专家相同的诊断标准.
- 需要进一步的研究和验证,以确保ADRC与可靠的DR查的临床标准保持一致.
- XAI方法对于理解和提高AI在医学诊断中的透明度至关重要.
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