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

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MLCN 2020: Keynote by Dr. Duygu Tosun

Title: Impact of AI and deep learning on imaging of neurodegenerative diseases Abstract: Biomarkers have become increasingly important to understand the biology of neurodegenerative diseases. We now see a paradigm shift recasting the definition of neurodegenerative disease in living people from syndromal to a biological construct. Effective implementation of such biological constructs though requires widespread availability of … Continue reading MLCN 2020: Keynote by Dr. Duygu Tosun

MLCN 2020: Keynote by Dr. Jorge Cardoso

Title: AI-enabled Neurology, Dealing with the real world Abstract: Recent developments in artificial intelligence and the availability of large scale medical imaging datasets allow us to learn how the human brain truly looks like from a biological, physiological, anatomical and pathological point-of-view. This learning process can be further augmented by diagnostic and radiological report data … Continue reading MLCN 2020: Keynote by Dr. Jorge Cardoso

MLCN 2020: Accepted Papers

Surface Agnostic Metrics for Cortical Volume Segmentation and Regression Samuel F Budd (Imperial College London)*; Prachi Patkee (King’s College); Ana Baburamani (King’s College); Mary Rutherford (KCL); Emma C Robinson (King’s College); Bernhard Kainz (Imperial College London) Abstract: The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current … Continue reading MLCN 2020: Accepted Papers

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