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ACM SAC- COMBI 2017 : The First ACM/SAC-COMBI 2017 Technical Track on Advances in COMputational Biomedical Imaging


When Apr 3, 2017 - Apr 7, 2017
Where Marrakesh
Submission Deadline Sep 29, 2016
Notification Due Nov 15, 2016
Final Version Due Nov 25, 2016

Call For Papers

Call for papers

Advances in COMputational Biomedical Imaging Track, COMBI 2017
The 32nd ACM SIGAPP Symposium on Applied Computing
April 3–7, 2017, Marrakesh, Morocco

Mohammed El Hassouni, Mohammed V University in Rabat, Morocco
Rachid Jennane, University of Orleans, France
Ayman El-Baz, University of Louisville, USA

Important dates
Papers and SRC Submission: September 29, 2016
Papers and SRC Notification: November 10, 2016
Camera-Ready Copies: November 25, 2016
Author Registration: December 10, 2016

32nd ACM SAC
For the past thirty-one years, the ACM Symposium on Applied Computing has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2017 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP) and is hosted by hosted by the University of Quebec at Montreal; Canada; University Cadi Ayyad (UCA) of Marrakesh, Morocco; National School of Engineering in Rabat (EMI), Rabat, Morocco; and National School of Applied Sciences (ENSA) of Kenitra, Morocco.

Scope and topics of the track
Nowadays, many modalities such as CT, X-ray scanners, MRI/fMRI, PET scan, etc. generate complex images with a large amount of data that are becoming extremely difficult to handle. This growing mass of data requires new strategies for the diagnosis of diseases and new therapies.
In recent years, particular attention has been paid to computational methods in biomedical imaging applications. Inspired by artificial intelligence, mathematics, biology and other fields, these methods can find relationships between different categories of this complex data and provide a set of tools for the diagnosis and monitoring of the disease.
This track aims to bring together researchers from academia and industry, to identify and discuss technical challenges, exchange novel ideas, explore enabling technologies, and report latest research efforts in the field of computational biomedical imaging.
The topics of the track include the following computational methods for biomedical imaging, but are not limited to:

Bio-inspired methods
Neural networks
Fuzzy sets
Metaheuristics optimization
Genetic algorithms
Neuro-inspired computing
Evolutionary algorithms
Machine, deep and manifold learning
Pattern recognition
Computer aided diagnosis
Time series analysis
Computational intelligence
Decision support systems
Data mining and visualization
Mobile technology for biomedical applications
Big data analytics for biomedical imaging
Biomedical image processing and analysis
Biomedical image registration
Brain-computer interface application
Augmented reality
Retrieval and indexing
Compressive sensing

Submission guidelines
Authors are invited to submit original papers on the topics covered by the track COMBI 2017. Submissions fall into the following categories:
Original and unpublished research work
Reports of innovative computing applications in the arts, sciences, engineering, and business areas
Reports of successful technology transfer to new problem domains
Reports of industrial experience and demos of new innovative systems
Peer groups with expertise in the track focus areas will blindly review submissions. Submission guidelines can be found on SAC 2017 Website. Prospective papers should be submitted to the track using the START submission system. Submission of the same paper to multiple tracks is not allowed. For more information please visit SAC 2017 Website.

Registration and publication
Paper registration is required, allowing the inclusion of the paper/poster in the conference proceedings. An author or a proxy attending SAC MUST present the paper: This is a requirement for the paper/poster to be included in the ACM/IEEE digital library. No-show of scheduled papers and posters will result in excluding them from the ACM/IEEE digital library

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