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Page 2 of 3 Prasath. Neuroimmunol Neuroinflammation 2018;5:1 I http://dx.doi.org/10.20517/2347-8659.2017.68

[10]
various neuroimaging data problems. A Siamese DL networks approach for detecting spinal metastasis
with a multi-resolution technique correctly detected 100% of lesions on a dataset of 26 sagittal MR images
from 14 males and 12 females (58 ± 14 years; mean ± SD). The DL network considered produced only
0.40 false positives (FPs) per case. Further, at a true positive (TP) rate of 90%, with aggregation FPs were
reduced from 0.375 FPs per case to 0.207 FPs per case obtaining 44.8% overall reduction. Although this
work was for MR images of the spine, the usage of a Siamese neural network with the aggregation strategy
promises to be an interesting approach that can also be adapted to brain MRI/fMRI imagery.

[11]
Utilizing domain-transfer convolutional neural networks, an end-to-end DL technique , shows great
promise since it overcomes the following problems of traditional classification and other DL based
methods: (1) the need for manual design of feature space; (2) effective feature vector classifier or segment
specific detection object and image patches; (3) large training datasets; (4) computing resources; and (5)
long waiting times for training a perfect deep model. An example classification of the Open Access Series
of Imaging Studies (OASIS)-MRI dataset showed good potential for such an approach’s generalizability.

Extreme learning machines is a variant of DL networks, and an application in resting state fMRI data
[12]
for schizophrenia was undertaken and experimental results indicated that near 90% accuracy was
obtained on a dataset of 72 patient images and 75 healthy controls (18 to 65 years) from the Center for
Biomedical Research Excellence (COBRE)’s raw anatomical and fMRI data on this difficult classification
[13]
problem. A DL pipeline applied to recognize Alzheimer’s disease using fMRI data obtained overall
highest accuracy of 96.86% on 28 patient images and 15 healthy controls (24 female and 19 male,
74.9 ± 5.7 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset.

Most of the CAD pipelines with DL techniques at their core utilize non-medical data to train due to
the lack of availability of massive labeled data. Recent advancements in natural image analysis with
DL methods are yet to be used for neuroimaging data and the challenges in obtaining the datasets/
annotations/labels, improvising/adapting DL networks, parameters setup, multi-modality generalization
pose remain to be solved. However, the recent advancements in deep learning based image analysis shows
great potential for analyzing MRI/fMRI imagery. Even with the limited results available so far in the
literature, with deep learning based CAD for neuroimaging data we believe the future is bright for solving
some of the hard neuroimage analysis problems.

DECLARATIONS
Authors’ contributions
Prasath VBS contributed solely to this letter.

Financial support and sponsorship
None.

Conflicts of interest
There are no conflicts of interest.

Patient consent
Not applicable.

Ethics approval
Not applicable.

Copyright
© The Author(s) 2018.