用变压器提高天花诊断:通过定量验证将可解释性和性能联系起来
Delal Şeker1, Abdulnasır Yıldız1
1Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir 21280, Turkey.
Diagnostics (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了先进的AI模型,视觉转换器 (ViT) 和数据效率图像转换器 (DeiT),用于麻疹分类,实现高精度. 一种新的混合可解释性方法提高了皮肤病学中的诊断可靠性.
科学领域:
- 皮肤病学中的人工智能
- 医学图像分析 医学图像分析
- 机器学习用于传染病.
背景情况:
- 鉴于与其他皮肤疾病的视觉相似性,天花的诊断具有挑战性.
- 现有的AI模型主要使用卷积神经网络 (CNN),仅限于使用变压器架构.
- 皮肤病学AI的解释性通常依赖于未经验证的热图技术.
研究的目的:
- 应用基于变压器的模型来分类麻疹.
- 为人工智能模型引入和评估一种新的混合可解释性方法.
- 提高AI在诊断皮肤病的透明度和临床相关性.
主要方法:
- 微调视觉转换器 (ViT) 和数据效率图像转换器 (DeiT) 用于二进制和多种类别的麻疹分类.
- 整合梯度加权类激活映射 (Grad-CAM),层级相关性传播 (LRP) 和注意力推广 (AR) 以实现模型解释性.
- 开发一种混合解释方法,通过主要组件分析 (PCA) 结合热图,并通过删除/插入指标评估可靠性.
主要成果:
- ViT模型表现出卓越的性能,实现AUC为0.9192的二进制和0.9784的多类分类.
- 混合可解释性方法 (Grad-CAM + LRP) 提供了比单个方法更具信息性的解释.
- 定量评估证实了混合解释的增强临床可靠性.
结论:
- 这项研究开创了使用具有系统评估混合可解释性的变压器模型来分类麻疹的方法.
- 该研究提高了AI在皮肤病学中的预测准确性和可解释性.
- 未来的研究应该专注于扩大数据集,并纳入临床元数据以获得更广泛的适用性.
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