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2018

Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

Li, Xiaoxiao, Dvornek, Nicha C., Zhuang, Juntang et al.

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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder.

Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment.
Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data-driven computational decision making procedure has not been previously explored.
Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier.

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E. Whitley et al
2002
Earlier work this paper cites.
Tzourio-Mazoyer et al
2002
Earlier work this paper cites.
S. M. Smith et al
2004
Earlier work this paper cites.
P. Ventola et al
2007
Earlier work this paper cites.
M. D. Kaiser et al
2010
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