用多阶段图学习提升小样本脑病诊断准确率
Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders
- 分预训练与微调两阶段，先自监督学习再监督诊断
- 在ABIDE I/II和ADHD数据集上准确率达0.63~0.93
- 适合小样本、标注少的神经发育障碍研究
深度监督模型在脑疾病诊断中受限于标注数据不足。为缓解这一问题，我们提出一种多阶段图学习框架：第一阶段在有限标注的fMRI数据上进行自监督图学习；第二阶段在监督下完成脑病诊断。在ABIDE I、ABIDE II和ADHD三个数据集（使用AAL1脑区划分）上的实验表明，该框架性能优于现有方法，准确率范围为0.7330~0.9321（ABIDE I）、0.7209~0.9021（ABIDE II）、0.6338~0.6699（ADHD），展现了优异的性能与泛化能力。
原文摘要 · Abstract (English)
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)
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