Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders

Autor:	Gao, Wenjing, Yang, Yuanyuan, Wei, Jianrui, Yin, Xuntao, Di, Xinhan
Rok vydání:	2024
Předmět:	Quantitative Biology - Neurons and Cognition Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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) Comment: Accepted by CVPR 2024 CV4Science Workshop (8 pages, 4 figures, 2 tables)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.05342 Zobrazit plný text záznamu View this record from Arxiv