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
- …
