基于改进VGG网络的弱监督细粒度阿尔兹海默症分类方法

针对阿尔兹海默症(AD)患者和正常(NC)人之间核磁共振成像(MRI)图像差别小、分类难度大的问题,提出了基于改进VGG网络的弱监督细粒度AD分类方法.该方法以弱监督数据增强网络(WSDAN)为基本模型,主要由弱监督注意力学习模块、数据增强模块及双线性注意力池化模块等构成.首先,通过弱监督力注意学习模块生成特征图和注意...

Weakly supervised fine-grained classification method of Alzheimer's disease based on improved visual geometry group network

In order to solve the problems of small difference of Magnetic Resonance Imaging （MRI） images between Alzheimer’s Disease （AD） patients and Normal Control （NC） people and great difficulty in classification of them， a weakly supervised fine-grained classification method for AD based on improved Visual Geometry Group （VGG） network was proposed. In this method， Weakly Supervised Data Augmentation Network （WSDAN） was took as the basic model， which was mainly composed of weakly supervised attention learning module， data augmentation module and bilinear attention pooling module. Firstly， the feature map and the attention map were generated through weakly supervised attention learning network， and the attention map was used to guide the data augmentation. Both the original image and the augmented data were used as the input data for training. Then， point production between the feature map and the attention map was performed by elements via bilinear attention pooling algorithm to obtain the feature matrix. Finally， the feature matrix was used as the input of the linear classification layer. Experimental results of applying WSDAN basic model with VGG19 as feature extraction network on MRI data of AD show that， compared with the WSDAN basic model， the proposed model only with image enhancement has the accuracy， sensitivity and specificity increased by 1.6 percentage points， 0.34 percentage points and 0.12 percentage points respectively； the model only using the improvement of VGG19 network has the accuracy and specificity improved by 0.7 percentage points and 2.82 percentage points respectively； the model combing the two methods above has the accuracy， sensitivity and specificity improved by 2.1 percentage points， 1.91 percentage points and 2.19 percentage points respectively.