Machine learning for dementia clinical forecasting from imaging and multimodal data

[Remi-Gau]

Added as an issue for book keeping

Source:
https://brainhack.ch/past_events/opengeneva2019/index.html#ML_tadpole

Leader:
Mazen Mahdi and Jonas Richiardi

The goal of this project is to rapidly develop and test algorithms to forecast disease evolution in Alzheimer's disease, using pre-extracted brain imaging markers from MRI (volume, thickness...), PET (metabolism, amyloid load...), Diffusion (diffusivity), liquid biomarkers, and clinical scores, provided by the TADPOLE challenge project.