Advanced Computational Methods in bioMedical Imaging and Population Health 

Track, ACMMIPH 2018

The 33nd ACM SIGAPP Symposium on Applied Computing

April 9 – 13, 2018, Pau, France


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 2018 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP) and is hosted by Unversité de Pau et des Pays de l'Adour (UPPA), Pau, France.

Aims and scope of the track

With the development of medical devices, a large ammount of data is generated that are becoming extremely difficult to handle. This growing mass of 
data  requires new strategies for different applications such as the biomedical imaging and population health. Among the useful tools in this context, we find the computation methods. 

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 on the application of the computational methods in the fields of biomedical imaging and population health.


The topics of the track include the following computational methods for biomedical imaging and population health, 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
  • Computational Epidemiology
  • Computational Response Planning
  • Contagion Modeling and Simulation
  • Computational Ecology
  • Data-Centric Disaster Preparedness
  • Health GIS
  • Data Mining & Machine Learning for Population Health Models
  • Deep Learning Models for Public Health Behavior Patterns
  • Trajectory and Pattern Analytics for Health-related Events
  • Real-time health/disease monitoring and visualization
  • Geospatial and Temporal Disease spreading model
  • Cognitive Computing for Public Health Intelligence
  • Big data analytics and Public Health Risks
  • Prediction models for Disease outbreak
  • IoT for Public Health Prediction and Response
  • Sensors, Crowdsourcing for public health
  • Semantic reasoning and inference
  • Security and Privacy in Public Health surveillance
  • Human mobility, transportation for epidemic studies
  • Smart Cities for Public Health Disaster preventions and response
  • Mobile Technologies for Health Disaster management
  • Online Social Media network analytics for health event detection and entity linking
  • Case studies of Intelligent Computational Methods in Public Healt