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Abstract #1872

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

Bonian Lu1 and Gopikrishna Deshpande1
1AU MRI Research Center, Electrical and Computer Engineering, Auburn University, Auburn, AL, United States

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.

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