MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal data

TL;DR

This paper introduces a novel distillation approach that transfers knowledge from multi-modal data to MRI-based models, enhancing Alzheimer's disease prediction especially when multi-modal data is scarce.

Contribution

It presents the first method to improve MRI-based Alzheimer's prediction by distilling knowledge from multi-modal data, addressing data limitations in clinical settings.

Findings

The proposed method outperforms existing MRI-only models.

Multi-instance probabilities better capture atrophy distributions.

Framework shows promise in data-limited scenarios.

Abstract

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.