Multimodal hierarchical multi-task deep learning framework for jointly predicting and explaining Alzheimer disease progression

arXiv:2404.03208v1 Announce Type: new
Abstract: Early identification of Mild Cognitive Impairment (MCI) subjects who will eventually progress to Alzheimer Disease (AD) is challenging. Existing deep learning models are mostly single-modality single-task models predicting risk of disease progression at a fixed timepoint. We proposed a multimodal hierarchical multi-task learning approach which can monitor the risk of disease progression at each timepoint of the visit trajectory. Longitudinal visit data from multiple modalities (MRI, cognition, and clinical data) were collected from MCI individuals …

abstract arxiv cognitive cs.lg deep learning deep learning framework disease eventually framework hierarchical identification multimodal progress risk type will