Iterative sparse and deep learning for accurate diagnosis of Alzheimer’s disease

作者：

Highlights：

• Propose the ISDL model for Alzheimer’s disease (AD) and mild cognitive impairment (MCI) diagnosis.

• Integrate sparse regression into a DFE module for joint deep feature extraction and critical cortical region identification.

• Update the parameters of the two modules alternatively and iteratively in an end-to-end manner.

• Provide a state-of-the-art solution to both AD-CN classification and MCI-to-AD prediction.

摘要

•Propose the ISDL model for Alzheimer’s disease (AD) and mild cognitive impairment (MCI) diagnosis.•Integrate sparse regression into a DFE module for joint deep feature extraction and critical cortical region identification.•Update the parameters of the two modules alternatively and iteratively in an end-to-end manner.•Provide a state-of-the-art solution to both AD-CN classification and MCI-to-AD prediction.

论文关键词：Alzheimer’s disease,Mild cognitive impairment,Deep learning,Sparse regression

论文评审过程：Received 7 July 2020, Revised 28 December 2020, Accepted 5 March 2021, Available online 15 March 2021, Version of Record 26 March 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.107944