  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 AD. Although previous studies have
shown that the fusion of multi-modal data can effectively improve the
prediction accuracy, their applications are largely restricted by the limited
availability or high cost of multi-modal data. Building an effective prediction
model using only magnetic resonance imaging (MRI) remains a challenging
research topic. In this work, we propose 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. In
contrast to existing distillation algorithms, the proposed multi-instance
probabilities demonstrate a superior capability of representing the complicated
atrophy distributions, and can guide the MRI-based network to better explore
the input MRI. To our best knowledge, this is the first study that attempts to
improve an MRI-based prediction model by leveraging extra supervision distilled
from multi-modal information. Experiments demonstrate the advantage of our
framework, suggesting its potentials in the data-limited clinical settings.
