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, include Spatio-temporal brain data analysis,Model scalability in large neuroimaging datasets,Unsupervised methods for stratifying brain disorders,Deep learning in clinical neuroimaging,Model uncertainty in clinical predictions. In the clinical neuroimaging track, we seek applications of existing machine learning approaches to address major challenges towards reaching precision medicine for brain disorders, e.g., Biomarker discovery,Biological validation of clinical syndromes and Brain aging.

Theme:

• Deep learning in clinical neuroimaging

• Spatio-temporal brain data analysis

• Biomarker discovery