Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders
Journal:
arXiv
Published Date:
Oct 7, 2024
Abstract
The insufficient supervision limit the performance of the deep supervised
models for brain disease diagnosis. It is important to develop a learning
framework that can capture more information in limited data and insufficient
supervision. To address these issues at some extend, we propose a multi-stage
graph learning framework which incorporates 1) pretrain stage : self-supervised
graph learning on insufficient supervision of the fmri data 2) fine-tune stage
: supervised graph learning for brain disorder diagnosis. Experiment results on
three datasets, Autism Brain Imaging Data Exchange ABIDE I, ABIDE II and ADHD
with AAL1,demonstrating the superiority and generalizability of the proposed
framework compared to the state of art of models.(ranging from 0.7330 to
0.9321,0.7209 to 0.9021,0.6338 to 0.6699)
