MLCN 2019

This year MLCN focuses on the application of machine learning on small sample size clinical neuroimaging data through novel methodologies for data harmonization and transfer learning. Topics of interests include but are not limited to:

  •        Transfer learning in clinical neuroimaging
  •        Domain adaptation in neuroimaging
  •        Data harmonization across sites
  •        Data pooling – practical issues
  •        Model stability in transfer learning
  •        Data prerequisites for successful transfer learning
  •        Cross-domain learning in neuroimaging
  •        Interpretability for transfer learning
  •        Unsupervised methods for domain adaptation
  •        Multi-site data analysis, from preprocessing to modeling
  •        Big data in clinical neuroimaging

Recent Posts

Keynote by Dr. Yong Fan

Title: Deep learning of MRI brain images for early prediction of Alzheimer’s disease dementia and cognitive decline Early prediction of Alzheimer’s disease (AD) and cognitive decline could promote timely interventions to slow or halt dementia progression. Although clinical criteria for mild cognitive impairment (MCI) and AD have been developed to formalize assessment of the gradual … Continue reading Keynote by Dr. Yong Fan

Accepted Papers

Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors Authors: Maria Ines Meyer (Technical University of Denmark); Ezequiel de la Rosa (Technical University of Munich); Koen Van Leemput (Technical University of Denmark); Diana Sima (icometrix, Leuven, Belgium) Abstract: With the increased need for multi-center magnetic resonance imaging studies, problems arise related to … Continue reading Accepted Papers

More Posts