Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion

• Published in: Medical image analysis Vol. 45; pp. 68 - 82
• Main Authors: Thung, Kim-Han, Yap, Pew-Thian, Adeli, Ehsan, Lee, Seong-Whan, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2018; Elsevier BV
• Subjects: Affinity; Alzheimer's disease; Classification; Cognition & reasoning; Cognitive ability; Conversion; Correlation analysis; Data imputation; Impairment; Longitudinal studies; Low-rank representation; Mathematical analysis; Matrix completion; Matrix methods; Medical imaging; Multi-task learning; Multitasking; Neuroimaging; Neurology; Noise reduction; Regression analysis; Subspace methods; Subspaces; Low-rank representation; Data imputation; Multi-task learning; Classification; Matrix completion

•Longitudinal MRI, PET and cognitive data are used jointly for pMCI prediction.•MCI subjects exhibit biological heterogeneity, and the data are incomplete and noisy.•Low rank affinity-pursuit denoising is used to denoise incomplete heterogeneous data.•Label and conversion time are predicted jointly...