模式识别(心理学)
机器学习
卷积神经网络
特征提取
特征(语言学)
神经影像学
分类器(UML)
上下文图像分类
棱锥(几何)
作者
Xingyu Gao,Feng Shi,Dinggang Shen,Manhua Liu
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2021-07-19
被引量:1
标识
DOI:10.1109/jbhi.2021.3097721
摘要
With the advance of medical imaging technologies, multimodal images such as magnetic resonance images (MRI) and positron emission tomography (PET) can capture subtle structural and functional changes of brain, facilating the diagnosis of brain diseases such as Alzheimers disease (AD). In practice, multimodal images may be incomplete since PET is often missing due to high financial cost or availability. Most of existing methods simply excluded subjects with missing data, which unfortunately reduced sample size. In addition, how to extract and combine multimodal features is still challenging. To address these problems, we propose a deep learning framework to integrate a task-induced pyramid and attention generative adversarial network (TPA-GAN) with a pathwise transfer dense convolution network (PT-DCN) for imputation and also classification of multimodal brain images. First, we propose a TPA-GAN to integrate pyramid convolution and attention module as well as disease classification task into GAN for generating the missing PET data with their MRI. Then, with the imputed multimodal brain images, we build a dense convolution network with pathwise transfer blocks to gradually learn and combine multimodal features for final disease classification. Experiments are performed on ADNI-1 and ADNI-2 datasets to evaluate our proposed method, achiving superior performance in image imputation and brain disease diagnosis compared to state-of-the-art methods.
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