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提高多发性硬化和脑小血管疾病的分类,使用可解释的注意力转移神经网络来改善分类

Wangshu Xu¹, Zhiwei Rong², Wenping Ma³

¹Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China.

Computers in biology and medicine

|May 15, 2024

概括

一个新的深度学习模型使用MRI扫描准确地区分了多发性硬化症 (MS) 和大脑小血管疾病 (cSVD). 这种可解释的人工智能工具有助于诊断,通过减少误诊风险,有可能改善患者的治疗结果.

科学领域:

神经成像是一种神经成像.
人工智能在医学中的应用
神经系统疾病神经系统疾病

背景情况:

多发性硬化症 (MS) 和脑小血管疾病 (cSVD) 具有相似的临床和成像特征,导致潜在的误诊.
错误诊断MS可能导致延迟或不正确的治疗,显著影响疾病的进展和患者的预后.
可靠的诊断辅助对于神经疾病的准确差异诊断至关重要.

研究的目的:

开发一个可解释的深度学习 (DL) 模型,使用T2加权的FLAIRMRI图像来区分MS和cSVD.
为了提高诊断准确度,并减少这些重叠的神经疾病患者的错误诊断率.
创建一个实用的成像工具,利用人工智能来改进神经系统疾病的分类.

主要方法:

利用来自ImageNet数据集的转移学习进行特征提取.
开发了一个DL模型,包含两个注意力模块,用于通过注意力地图进行强大的特征学习和可解释性.
通过使用焦点损失来解决类不平衡,对112名多发性硬化硬化疾病患者和321名cSVD患者进行了端到端的模型训练.

主要成果:

拟议的DL模型实现了平均平衡精度为86.06%和平均AUC为98.78%,优于其他DL方法.
注意热图显示了该模型能够专注于大脑图像中的特征性损伤标志的能力.
该模型证明有效地区分MS和cSVD,提供可解释的见解.

结论:

开发的可解释的DL模型可以作为诊断成像的实用辅助工具来区分MS和cSVD.
这种人工智能驱动的方法,利用常规的MRI技术,显示出改善神经疾病准确分类的巨大潜力.
该模型通过注意力图的可解释性为临床医生提供了有价值的信息,有助于差异诊断和患者管理.

关键词:

注意力机制注意力机制大脑小血管疾病大脑小血管疾病可以解释性解释性机器学习是机器学习.多发性硬化症是多发性硬化症.

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