Chong Yaw Wee

TL;DR: This study attempts to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance and indicates that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently.

TL;DR: This paper proposes a novel methodological architecture that combines deep learning and state-space modelling, and applies it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis, and designs a Deep Auto-Encoder to discover hierarchical non-linear functional relations among regions, which transform the regional features into an embedding space, whose bases are complex functional networks.

TL;DR: In this article, a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI).

TL;DR: This work proposes an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls and found that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies.

TL;DR: This work proposes a novel multi-task feature selection method that treats feature selection from each modality as a separate task and further impose a constraint for preserving the inter-modality relationship, besides separately enforcing the sparseness of the selected features from eachmodality.