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

Recent Posts

Keynote 1: Christos Davatzikos

Title:  Machine learning in neuroimaging: A journey from mass-univariate methods to multi-variate imaging signatures of diseases, in search of precision diagnostics Abstract: In this talk, I will briefly provide a historical perspective of the past 20 years, in which we saw the transition from hypothesis-based to big-data-driven methods of analysis of brain images. This journey … Continue reading Keynote 1: Christos Davatzikos

Accepted Papers

Alzheimer’s Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes Clement Abi Nader, Nicholas Ayache, Philippe Robert, and Marco Lorenzi Alzheimer’s disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain’s morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most … Continue reading Accepted Papers

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