Interpretation of deep non-linear factorization for autism
Autism, a neurodevelopmental disorder, presents significant challenges for diagnosis and classification. Despite the widespread use of neural networks in autism classification, the interpretability of their models remains a crucial issue. This study aims to address this concern by investigating the...
Main Authors: | Boran Chen, Bo Yin, Hengjin Ke |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-06-01
|
Series: | Frontiers in Psychiatry |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1199113/full |
Similar Items
-
Identification of robust and interpretable brain signatures of autism and clinical symptom severity using a dynamic time-series deep neural network
by: K. Supekar, et al.
Published: (2021-04-01) -
Inter-regional brain communication and its disturbance in autism
by: Sarah E. Schipul, et al.
Published: (2011-02-01) -
Under-reactive but easily distracted: An fMRI investigation of attentional capture in autism spectrum disorder
by: Brandon Keehn, et al.
Published: (2016-02-01) -
A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
by: Fatima Zahra Benabdallah, et al.
Published: (2023-05-01) -
Deep Multimodal Learning for the Diagnosis of Autism Spectrum Disorder
by: Michelle Tang, et al.
Published: (2020-06-01)