计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 140-142.

• 模式识别与图像处理 • 上一篇    下一篇

基于3D-PCANet的阿尔兹海默病辅助诊断

李书通,肖斌,李伟生,王国胤   

  1. 重庆邮电大学计算智能重庆市重点实验室 重庆400065
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:李书通(1993-),男,硕士生,主要研究方向为医学图像处理;肖 斌(1982-),男,博士,副教授,主要研究方向为图像处理、模式识别及数字水印等,E-mail:xiaobin@cqupt.edu.cn;李伟生(1976-),男,博士,教授,博士生导师,主要研究方向为智能信息处理与模式识别;王国胤(1970-),男,博士,教授,博士生导师,主要研究方向为粗糙集、粒计算、数据挖掘、机器学习、神经网络、认知计算、模式识别等。
  • 基金资助:
    国家自然科学基金(61572092),国家自然科学基金-广东联合基金(U1401252),国家重点研发计划(2016YFC1000307-3)资助

Diagnosis of Alzheimer’s Disease Based on 3D-PCANet

LI Shu-tong,XIAO Bin,LI Wei-sheng,WANG Guo-yin   

  1. Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 深度学习在医疗辅助诊断上发挥着越来越重要的作用。然而,深度学习在辅助诊断的过程中,常常会遇到数据标签不足的问题。研究了以非监督深度学习的思想来解决数据标签不足的问题,提出了一种非监督学习方法——3D-PCANet,以对阿尔兹海默病症的MRI图像进行计算机的辅助诊断。该方法使用三维的MRI图像作为数据源。实验结果显示,3D-PCANet算法在阿尔兹海默病诊断中实现了良好的分类效果。

关键词: 阿尔兹海默病的诊断, 卷积神经网络, 迁移学习, 深度学习

Abstract: Deep learning technologies play more and more important roles in computer aided diagnosis (CAD) in medicine.However,they always face the problem that insufficient labeled data is available for deep learning methods to learn the millions of weights.This paper took the idea of non-supervised to solve the problem on limited labeled labels,and proposed a 3D-PCANet method from aspects of unsupervised deep learning for computer aided AD prediction on limited labeled MRI image.Simultaneously,full 3-D view of MRI images are used in the proposed methods.Experimental results show that the proposed method achieves promising performance in AD prediction.

Key words: Alzheimer’s disease prediction, Convolutional neural network, Deep learning, Transfer learning

中图分类号: 

  • TP301
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