MLCN 2022

(Join us in room Leo 4 at 12:30 SGT on September 18)

The 5th international workshop on machine learning in clinical neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track).

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

  • Big data
  • 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, the applications of existing machine learning algorithms are evaluated to move towards precision medicine for complex brain disorders. The discovery of biological markers in medicine is an important challenge across different fields and various experimental procedures and designs are used to detect biological signatures that can be utilized for improvement in diagnostic, treatment, or for other beneficial ends. However, for most complex brain disorders, we do not have reliable biomarkers today. The application of advanced machine learning methods may help reach this goal. Therefore, we invite the community to submit conference contributions on machine learning approaches with the goal to improve our understanding of complex brain disorders, moving the field closer to precision medicine. 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
  • The presentation of clinical neuroimaging databases to stimulate developments in machine learning

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