[Comp-neuro] Call for Papers: Special Session on Computational Methods for Neuroimaging Analysis, 6-8 September, Portugal

Tiago Azevedo tiago.azevedo at cl.cam.ac.uk
Tue May 22 15:32:37 CEST 2018

Dear all,

Below find the call for papers for the special session "Computational 
Methods for Neuroimaging Analysis". It will be be held during the 15th 
International Conference on Computational Intelligence methods for 
Bioinformatics and Biostatistics, at Caparica, Portugal, from 6 to 8 

**Important Dates**
Paper submission deadline: 10 June 2018

Acceptance notification: 9 July 2018

Author registration due: 20 July 2018

Camera ready due: 29 July 2018

Conference: 6-8 September 2018


**Aim and scope**
There is an increasing need for the application of machine learning (ML) 
techniques which can perform image processing operations such as 
segmentation, coregistration, classification and dimensionality 
reduction in the field of neuroimaging. Although the manual approach 
often remains the golden standard in some tasks (like segmentation), ML 
can be utilised to automate and facilitate the work of researchers and 
clinicians. Frequently used techniques include support vector machines 
(SVMs) for classification problems, graph-based methods for brain 
network analysis and recently artificial neural networks (ANNs).

Deep ANNs, i.e. deep learning, have proved to be very successful in 
computer vision tasks owing to the ability to automatically extract 
hierarchical descriptive features from input images. It has also been 
used in the medical and neuroimaging domains for automatic disease 
diagnosis, tissue segmentation and even synthetic image generation. The 
issue, however, is the relative sample paucity in typical neuroimaging 
datasets which leads to poor generalisation considering the high number 
of parameters employed in typical deep neural networks. Consequently, 
parameter- efficient design paradigms ought to be created.

Another approach to investigate degeneration is the study and mapping of 
the neural connections in the brain known as the connectome. The 
connectome can be seen as a matrix representing all possible pairwise 
connections between different neural areas. Researchers study both the 
structural and functional connectivity in order to understand important 
brain patterns, such as how the connectome impacts the dynamics of 
disease spreading, ageing and learning.

Topics of interest includes but are not limited to:

• Machine learning techniques for segmentation, coregistration, 
classification or dimensionality reduction of neuroimages 

• Deep learning for neuroimaging analysis 

• Brain network analysis 

• Applications of graph theory to MRI and fMRI data 

• Applications of machine learning methodologies for neurodegenerative 
disease studies 

• Computational modelling and analysis of neuroimaging 

• Methods of analysis for structural or functional connectivity 

• Development of new neuroimaging tools

**Session chairs**
Tiago Azevedo, University of Cambridge, UK

Giovanna Maria Dimitri, University of Cambridge, UK

Pietro Liò, University of Cambridge, UK

Angela Serra, University of Salerno, Italy

Simeon Spasov, University of Cambridge, UK

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