Connectionists: SI-COGN (IF: 5.42) on Advances in Multi-modal Deep and Shallow Neural Networks for Neuroimaging Applications

[M Tanveer]

*Call for Papers*


*Journal:  Cognitive Computation, Springer (IF: 5.42)*



*Special Issue Title:  Advances in Multi-modal Deep and Shallow Neural
Networks for Neuroimaging Applications*



*We welcome your submissions.*



Aim and Motivation:

Over the past few decades, there has been an exponential increase in the
volume, veracity and variety of multi-modal Big data generated from medical
imaging applications. This has led to growing challenges for machine
learning researchers to effectively extract hidden features and reduce
artifacts automatically from images, in order to enhance disease
classification, diagnosis, prognosis, segmentation and risk assessment
(such as ionizing radiation exposure and side effect of contrast agents).
Most existing solutions to these problems are suboptimal owing to risks
associated with model training that often lead to inaccurate image
acquisition and analysis on account of e.g. overfitting, noise in image,
class imbalance and inappropriate features selection. Hence, automated and
reliable quality control in medical imaging is a crucial factor for future
widespread clinical deployment of machine learning based solutions.

In the context of neuroimaging applications, neuroimaging scans are being
increasingly and contextually used, along with social, clinical and
laboratory data, to detect and diagnose neurological diseases, such as
Alzheimer’s disease, Multiple sclerosis, Parkinson’s disease etc.  The
sources of neuroimaging modalities are from a wide variety of clinical
settings, including electrocardiography (ECG), electroencephalography
(EEG), magnetic resonance imaging (MRI), functional MRI (fMRI), positron
emission tomography (PET). Recent literature has shown the potential of
deep and shallow neural network-based multimodal learning algorithms to
address a range of neuroimaging challenges on account of their automatic
multimodal feature selection, learning and generalisation capabilities.

This timely special issue aims to bring together world-leading cutting-edge
research (from both academia and industry) on multimodal neural network
algorithms, including integrated deep and shallow models, that can increase
the diagnosis and prognosis accuracy in analysis of neuroimaging Big data.


Topics: Topics include but are not limited to:

   - Multimodal deep neural networks
   - Multimodal shallow neural networks
   - Integrated deep and shallow models for multimodal learning
   - Real-time segmentation, clustering and classification
   - Sparse, interpretable and privacy preserving data analytics
   - Real-time Image acquisition, resolution, registration and production
   - Automated multimodal artifacts reduction in neuroimaging Automated
   quality assessment and clinical validation models
   - Emerging multimodal neuroimaging applications




Deadlines:

Submissions deadline: April 30, 2022

First notification of acceptance: June 30, 2022

Submission of revised papers: August 30, 2022

Final notification to authors: October 30, 2022

Rolling publication of special issue: late 2022/early 2023


Guest Editors:

M. Tanveer (Coordinator), Indian Institute of Technology Indore, India,
Email: mtanveer at iiti.ac.in

Chin-Teng Lin, University of Technology Sydney, Australia, Email:
Chin-Teng.Lin at uts.edu.au

Yu-dong Zhang, University of Leicester (UK), Email: yudongzhang at ieee.org

Kaizhu Huang, Xi’an Jiaotong-Liverpool University, China, Email:
kh476 at duke.edu
----------------------------------------------------------
Dr. M. Tanveer (General Chair - ICONIP 2022)
Associate Professor and Ramanujan Fellow
Department of Mathematics
Indian Institute of Technology Indore
Email: mtanveer at iiti.ac.in
Mobile: +91-9413259268
Homepage: http://iiti.ac.in/people/~mtanveer/

Associate Editor: IEEE TNNLS (IF: 10.45).

Associate Editor: Pattern Recognition, Elsevier (IF: 7.74).

Action Editor: Neural Networks, Elsevier  (IF: 8.05).

Board of Editors: Engineering Applications of AI, Elsevier (IF: 6.21).

Associate Editor: Neurocomputing, Elsevier  (IF: 5.72).

Editorial Board: Applied Soft Computing, Elsevier  (IF: 6.72).

Associate Editor: Cognitive Computation, Springer (IF: 5.42).

Associate Editor: International Journal of Machine Learning & Cybernetics
(IF: 4.012).
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