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
