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Prasath. Neuroimmunol Neuroinflammation 2018;5:1 Neuroimmunology
DOI: 10.20517/2347-8659.2017.68 and Neuroinflammation

Letter to Editor Open Access

Deep learning based computer-aided diagnosis
for neuroimaging data: focused review and future
potential

V. B. Surya Prasath 1,2,3

1 Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia,
Columbia, MO 65211, USA.
2 Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH 45229, USA.
3 Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH 45267, USA.

Correspondence to: Dr. V. B. Surya Prasath, Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer
Science, University of Missouri-Columbia, Columbia, MO 65211, USA. E-mail: prasaths@missouri.edu

How to cite this article: Prasath VBS. Deep learning based computer-aided diagnosis for neuroimaging data: focused review
and future potential. Neuroimmunol Neuroinflammation 2018;5:1. http://dx.doi.org/10.20517/2347-8659.2017.68

Received: 25 Dec 2017 Accepted: 28 Dec 2017 Published: 12 Jan 2018
Science Editor: Athanassios P. Kyritsis Copy Editor: Lu Liu Production Editor: Huan-Liang Wu

Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance
imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided
diagnosis (CAD) tools in neuroscience [1-4] . Recently, the advancements in machine learning techniques
combined with the wide availability of computational power have proven to be efficient in solving
previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the
usage of deep (artificial) neural network architectures that led robust learning based techniques to attack
[5-8]
challenging problems such as segmentation and classification in neuroimaging data .
Many of the impressive results obtained in CAD using deep learning (DL) techniques utilize mainly
image datasets. DL networks typically require annotations of several images for employing supervised
learning techniques and are one of the roadblocks in employing these state of the art networks in various
classification tasks in MRI/fMRI. However, unsupervised learning techniques within the DL paradigm are
now being employed in natural image classification with a lot of success and we believe the adaptability of
these to the neuroimaging data are required to attack challenging neuroimage analysis problems.

[9]
A stacked denoising auto encoders approach that is an unsupervised learning technique was used for
brain tumor segmentation in MRI imagery. The experimental results showed that using this particular
approach is as good as using supervised learning based DL techniques that require accurate image-based
annotations. This indicates that we can use different unsupervised learning in DL networks variants for

© The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0
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