MLCN 2018

MLCN aims to bring together top-notch researchers in machine learning and clinical neuroscience to discuss and hopefully bridge the existing gap in applied machine learning in clinical neuroimaging. This year, the main objective is to shed light on the opportunities and challenges in the structure-aware modeling of neuroimaging data in both encoding and decoding settings. We are looking forward to receiving original and high-quality contributions on both methodological developments and applications of machine learning in analyzing clinical neuroimaging data. Topics of interests include but are not limited to:

  •        Applications of spatio-temporal modeling in predictive clinical neuroscience
  •        Spatial penalization in decoding clinical neuroimaging data
  •        Spatial statistics in neuroimaging
  •        Learning with structured inputs and outputs in clinical neuroscience
  •        Multi-task learning in analyzing structured neuroimaging data
  •        Deep learning in analyzing structured neuroimaging data
  •        Model stability and interpretability in clinical neuroscience