Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?. (arXiv:2208.08902v1 [cs.LG])

Research in machine learning for autism spectrum disorder (ASD)
classification bears the promise to improve clinical diagnoses. However, recent
studies in clinical imaging have shown the limited generalization of biomarkers
across and beyond benchmark datasets. Despite increasing model complexity and
sample size in neuroimaging, the classification performance of ASD remains far
away from clinical application. This raises the question of how we can overcome
these barriers to develop early biomarkers for ASD. One approach might be to
rethink how we …

arxiv autism classification graph graph representation learning lg representation representation learning unsupervised