Connectionists: CFP "Machine Learning Methods Applied to Medicine" SS @ IJCNN 2017

[Beatriz Remeseiro López]

[Apologies if you receive multiple copies of this CFP] 


Call for papers: special session on "Machine Learning Methods Applied to Medicine" at IJCNN 2017 


International Joint Conference on Neural Networks (IJCNN 2017) 

May 14-19, 2017 - Anchorage, Alaska (USA) - http://www.ijcnn.org/ 


Machine Learning Methods Applied to Medicine 


Organized by: Veronica Bolon-Canedo, Amparo Alonso-Betanzos (University of A Coruña, Spain), Beatriz Remeseiro (University of Barcelona, Spain), Aurélio Campilho (University of Porto, Portugal) 


Machine learning has been an active research area in the last decades finding success in many different applications, among them in medical problems. Since machine learning is capable of automating manual processes which practitioners have to carry out --usually time-consuming and subjective--, its use can save time for practitioners and provide unbiased and repeatable results. Furthermore, it is common that data in medicine have large dimensionality but reduced sample size, making even more necessary the use of advanced machine learning techniques for clinical interpretation and analysis. 


The aim of this special session is to investigate the use of different machine learning techniques or approaches applied to medical problems. Additionally, it provides a platform for academics and clinical researchers to present and share their cutting-edge methods to deal with medical applications, as well as discussing the new challenges that have recently emerged in this exciting cross-disciplinary field . The topics of interest include, but are not limited to: 


    * 

New challenges in machine learning for medicine 
    * 

Machine learning for personalized medicine 
    * 

Artificial intelligence in medicine 
    * 

Clinical interpretation and analysis 
    * 

Computer-aided detection and diagnosis 
    * 

Decision support systems 
    * 

Feature selection and extraction in medicine 
    * 

Big data in healthcare 
    * 

Deep learning in large-scale datasets 
    * 

Learning in uncertainty labeled data 
    * 

Learning from sparse/missing/imbalanced data 
    * 

Biomedical signal and image analysis 
    * 

Bioinformatics and microarray analysis 
    * 

Brain-computer interfaces 



Submitted papers will be reviewed according to the IJCNN reviewing process and will be evaluated on their scientific value: originality, correctness, and writing style. 




IMPORTANT DATES: 

Paper submission deadline: November 15, 2016 

Paper decision notification: January 20, 2017 

Camera-ready submission: February 20, 2017 IJCNN conference: May 14-19, 2017 

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