Jie Zhang’s ISBI2018 Paper is Accepted, Congratulation!

Jie Zhang*, Yanshuai Tu, Qingyang Li, Richard Caselli, Paul Thompson, Jieping Ye, Yalin Wang, 2018 IEEE International Symposium on Biomedical Imaging

Abstract: Cortical thickness estimation performed via magnetic resonance imaging (MRI) is an effective measure of brain atrophy in preclinical individuals at high risk for Alzheimer’s disease (AD). However, the high dimensionality of individual cortical thickness data coupled with small population samples make it challenging to perform cortical thickness feature selection for AD diagnosis and prognosis. Thus far, there are very few methods that can accurately predict future clinical scores using longitudinal cortical thickness measures. In this paper, we propose an unsupervised dictionary learning algorithm, termed Multi-task Sparse Screening (MSS) that produces improved results over previous methods within this problem domain. Specifically, we formulate and solve a multi-task proablem using extracted top-significant features from the ADNI longitudinal data. Empirical studies on publicly available longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset () demonstrate improved correlation coefficients and root mean square errors, when compared to other algorithms.