Call for papers

New Submission deadline !!! September 27, 2016 (October 7, 2016)

Advances in COMputational Biomedical Imaging Track,  COMBI 2017

The 32nd ACM SIGAPP Symposium on Applied Computing

Apri 3 – 7, 2017, Marrakesh, Morocco


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.

Aims and scope 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.

The advances in computational biomedical imaging 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