There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Lequan Yu |
| Institution: | The University of Hong Kong |
| Department: | Department of Statistics and Actuarial Science |
| Country: | |
| Proposed Analysis: | Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI patients of high risks converting to Alzheimer’s disease (AD), is essential for preventing or slowing the progression of A, which has an important clinical and social impact. Although previous studies have worked on this problem, they merely use image data (MRI) or simply fuse the image and tabular data, without deeply consider the relationship between them for a more comprehensive analysis. In this project, we aim to design advanced machine learning algorithms to effectively fuse multi-modal data for improving prediction accuracy. Also, we will study how to build an effective prediction model using only a multi-modal multi-instance distillation scheme, which aims to distill the knowledge learned from multi-modal data to an MRI-based network for MCI conversion prediction. |
| Additional Investigators |
