A robust transfer learning method to improve early diagnosis of autism spectrum disorder classification

Overfitting, the main issue that constrains the validity and generalizability of machine-learning in neuroimaging-based diagnostic-classification, is in part due to small sample-sizes in relation to what is required for generalization. Even with data aggregation (such as in ABIDE), the relatively smaller sample-sizes are a result of the fact that it is difficult/expensive to acquire data from clinical-populations. With healthy-controls, we have comparatively larger samples available. Therefore, we propose to address overfitting by using larger healthy-samples (HCP) to learn the neural signature of healthy-controls, with the aim of transferring that learning into the context of discriminating Autism from healthy-controls.