Deep Learning in Neuroimaging: Overcoming Challenges With Emerging Approaches
Deep learning (DL) is of great interest in psychiatry due its potential yet largely untapped ability to utilize multidimensional datasets (such as fMRI data) to predict clinical outcomes. Typical DL methods, however, have strong assumptions, such as large datasets and underlying model opaqueness, th...
Main Authors: | Jason Smucny, Ge Shi, Ian Davidson |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-06-01
|
Series: | Frontiers in Psychiatry |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.912600/full |
Similar Items
-
Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis
by: Jason Smucny, et al.
Published: (2022-01-01) -
Deep learning for neuroimaging: a validation study
by: Sergey M Plis, et al.
Published: (2014-08-01) -
Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal
by: Zafar Iqbal, et al.
Published: (2025-01-01) -
A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
by: Hyunseok Lee, et al.
Published: (2024-05-01) -
Counterfactual Explanation of Brain Activity Classifiers Using Image-To-Image Transfer by Generative Adversarial Network
by: Teppei Matsui, et al.
Published: (2022-03-01)