MLCN 2020

The 3rd international workshop on machine learning in clinical neuroimaging (MLCN2020) aims to bring together the top researchers in both machine learning and clinical neuroimaging. This year the workshop is organized in two tracks 1) machine learning and 2) clinical neuroimaging.

The machine learning track seeks novel contributions that address current methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous clinical neuroimaging data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to:

  • Spatio-temporal brain data analysis
  • Structural data analysis
  • Graph theory and complex network analysis
  • Longitudinal data analysis
  • Model stability and interpretability
  • Model scalability in large neuroimaging datasets
  • Multi-source data integration and multi-view learning
  • Multi-site data analysis, from preprocessing to modeling
  • Domain adaptation, data harmonization, and transfer learning in neuroimaging
  • Unsupervised methods for stratifying brain disorders
  • Deep learning in clinical neuroimaging
  • Model uncertainty in clinical predictions

In the clinical neuroimaging track, we invite the community to submit applications of existing machine learning approaches to address major challenges towards reaching precision medicine for brain disorders. Topics of interest include but are not limited to:

  • Biomarker discovery 
  • Refinement of nosology and diagnostics 
  • Biological validation of clinical syndromes 
  • Treatment outcome prediction 
  • Course prediction 
  • Analysis of wearable sensors
  • Neurogenetics and brain imaging genetics
  • Mechanistic modeling
  • Brain aging

Recent Posts

Keynote by Dr. Pamela Douglas

TITLE: On the Stability of Deep Learning Representations for Neuroimaging The increasing use of deep neural networks (DNNs) has motivated a parallel endeavor: the design of adversaries that profit from successful misclassifications.  However, not all adversarial examples are crafted for malicious purposes.  For example, real-world systems often contain physical, temporal, and sampling variability across instrumentation and … Continue reading Keynote by Dr. Pamela Douglas

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

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