[Article] A semi-supervised autoencoder for autism disease diagnosis.
summary : In this paper, they tried to diagnose ASD using semi-supervised autoencoder (SSAE). SSAE tries to reconstruct the features that are specifically important for downstream task (ASD vs HC), which is conceptually different than conventional AE. With sparsity constraints, the model can overcome SOTA performance in ABIDE classification. With a little bit of fine-tuning(ex. sparsity control), this method could be applied for our research using CHA data or p-factor related future research.