Workshop: “Deep Learning and Tensor/Matrix Decomposition for Applications in Neuroscience”, November 17th, Singapore

Keynote Speakers (in progress)

tonioball-1 cuntai-guanimage002 alexander-binder-large
Dr. Tonio Ball 
Principal Investigator
(Translational Neurotechnology Lab
University Freiburg, Germany)
Dr. Cuntai Guan
Professor, Co-Director (Rehabilitation
Research Institute of Singapore)
Co-Chair (Nanyang Technological
University)
Dr. Alexander Binder
Assistant Professor (Singapore
University of Technology
and Design, SUTD)
Title of talk:“Deep learning for EEG analysis” Title of talk:“Deep Learning on Motor Image 
Brain Computer Interfaces”
Title of talk:“Explaining Decisions of deep neural 
networks”

The IEEE International Conference on Data Mining series (ICDM) has established itself as the world’s premier research conference in data mining. The main goal of current workshop is to bring together academics, researchers and practitioners to discuss and reflect on recent challenges in neuroscience in the context of Deep Learning.

Important Dates

All deadlines are at 11:59PM Pacific Time.

Workshop paper submissions August 7, 2018
Workshop paper notification September 4, 2018
Workhsop date November 17, 2018

Aims and Scope of the Workshop

The workshop is oriented to all potential applications of deep learning and matrix/tensor decomposition and networks in feature extraction, classification, recognition, segmentation, enhancing, clustering, anomaly detection, and prediction of brain and behavior data – as applied to the multi-modal brain data (MRI/fMRI/CT, EEG/MEG, and biomarker assays), especially for mental disorders. Special focus will be made on the practical aspects of how to design and train deep neural networks with appropriate reduction of the dimensionality, to achieve high classification performance and reliability.
Because of recent breakthroughs in machine learning, especially deep neural networks, it is expected that physicians will be able to completely rely on the machine interpretation of MRIs, CT, PET scans using deep learning in the nearest future. Though deep neural networks have revolutionized computer vision through the end-to-end learning (i.e., learning from the raw data), it is still difficult to accomplish the early detection of the major neurodegenerative diseases (such as ADHD, Autism, or Alzheimer’s) with the neural networks today, partially due to the need for development of optimization techniques in order to work with the Big Data in the most efficient way. These and related topics will be addressed at this workshop.

Call for Papers

We aim for a focus on the applications of Deep Learning to analysis of neuroimaging data. Topics of interests for the workshop include, but are not limited to:

  • Magnetic Resonance Imaging (MRI) / functional Magnetic Resonance Imaging (fMRI)
  • Electroencephalography (EEG)
  • Magnetoencephalography (MEG)
  • Positron Emission Tomography (PET)
  • Near-infrared spectroscopy (NIRS)
  • Computed Tomography (CT)
  • Behavioral Data
  • Physiological Data
  • Electromyography (EMG)

Submission Guidelines

Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format (link), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. For further information, please visit the ICDM guidelines page http://icdm2018.org/calls/call-for-papers/.

Manuscripts must be submitted electronically. We do not accept email submissions. Online submission system is available here.

Program Committee (in progress)

Contact information of the organizers

Mailing address (common)

Skoltech
1 Nobel str., Moscow,
121205, Russia
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