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4D spatio-temporal deep learning with 4D fMRI data for autism spectrum disorder classification
Citation Link: https://doi.org/10.15480/882.2732
Publikationstyp
Conference Paper
Date Issued
2019-07
Sprache
English
Institut
TORE-DOI
TORE-URI
Start Page
1
End Page
4
Article Number
Abstract Paper 129
Citation
2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019, Abstract Paper 129
Contribution to Conference
Publisher Link
Publisher
Information Extraction and Synthesis Laboratory, College of Information and Computer Science, University of Massachusetts Amherst
Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brain changes related to the disorder. Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD from spatio-temporal fMRI images. Typically, these methods have either focused on temporal or spatial information processing. Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D convolutional neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65
DDC Class
004: Informatik
600: Technik
610: Medizin
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