Deep learning-based feature representation for AD/MCI classification.

Heung Il Suk, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In recent years, there has been a great interest in computer-aided diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). Unlike the previous methods that consider simple low-level features such as gray matter tissue volumes from MRI, mean signal intensities from PET, in this paper, we propose a deep learning-based feature representation with a stacked auto-encoder. We believe that there exist latent complicated patterns, e.g., non-linear relations, inherent in the low-level features. Combining latent information with the original low-level features helps build a robust model for AD/MCI classification with high diagnostic accuracy. Using the ADNI dataset, we conducted experiments showing that the proposed method is 95.9%, 85.0%, and 75.8% accurate for AD, MCI, and MCI-converter diagnosis, respectively.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages583-590
Number of pages8
Volume16
EditionPt 2
Publication statusPublished - 2013 Jan 1
Externally publishedYes

Fingerprint

Alzheimer Disease
Learning
Prodromal Symptoms
Cognitive Dysfunction

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Suk, H. I., & Shen, D. (2013). Deep learning-based feature representation for AD/MCI classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 16, pp. 583-590)

Deep learning-based feature representation for AD/MCI classification. / Suk, Heung Il; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 2. ed. 2013. p. 583-590.

Research output: Chapter in Book/Report/Conference proceedingChapter

Suk, HI & Shen, D 2013, Deep learning-based feature representation for AD/MCI classification. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 16, pp. 583-590.
Suk HI, Shen D. Deep learning-based feature representation for AD/MCI classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 16. 2013. p. 583-590
Suk, Heung Il ; Shen, Dinggang. / Deep learning-based feature representation for AD/MCI classification. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 2. ed. 2013. pp. 583-590
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