Robust deep learning for improved classification of AD/MCI patients

Feng Li, Loc Tran, Kim Han Thung, Shuiwang Ji, Dinggang Shen, Jiang Li

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 6.2% on average as compared to classical deep learning methods.

Original languageEnglish
Pages (from-to)240-247
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2014

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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