Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis

Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically small due to the considerable time, cost, and resource...

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Bibliographic Details
Main Authors: Jason Smucny, Ge Shi, Tyler A. Lesh, Cameron S. Carter, Ian Davidson
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:NeuroImage: Clinical
Subjects:
Cognitive control
Ensemble learning
fMRI
Machine learning
Schizophrenia
Transfer learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158222002790

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http://www.sciencedirect.com/science/article/pii/S2213158222002790

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