深度迁移学习辅助的阿尔兹海默氏症早期诊断

DEEP TRANSFER LEARNING-ASSISTED EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE

目前越来越多的老人正在遭受着阿尔茨海默氏症AD(Alzheimer's Disease)带来的痛苦。临床研究显示,轻度认知障碍MCI(mild cognitive impairment)转化为阿尔兹海默氏症的概率很高,但是若能在MCI阶段对其进行药物治疗是可以康复的,因此提高根据核磁共振图像MRI医学图像诊断的准确率很有必要有。由于医学领域的特点,构建带标注信号的一定规模的数据集非常困难,导致现有机器学习/深度学习方法难以应用于医学影像分析以至于深度学习的结果并不理想。采用数据增广方式对原来数据集的规模进行一定数量的扩充。然后采用一种针对MRI识别的端到端的深度神经网络分类器(MCINet)。结合迁移学习方式对MCINet模型进行有效训练而不至于过拟合。实验结果表明,该方法在较少带标记训练样本的情形下,也获得较高的准确率。

At present,more and more elderly people are suffering from Alzheimer's Disease(AD).Clinical studies have shown that mild cognitive impairment(MCI)has a high probability of transforming into AD,but if it can be treated with medication at the stage of MCI,it can be recovered.Therefore,it is necessary to improve the diagnostic accuracy of medical images based on MRI.Because of the characteristics of medical field,it is very difficult to construct a certain scale data set with annotated signals,which makes the existing machine learning/deep learning difficult to apply to medical image analysis,so that the results of deep learning are not ideal.We used data augmentation to expand the size of the original data set.Then we used an end-to-end deep neural network classifier(MCINet)for MRI recognition.And we combined transfer learning to train the MCINet model effectively without over-fitting.The experimental results show that the method has a high accuracy in the case of fewer labeled training samples.