We study how to fuse heterogeneous information in a meaningful manner to leverage information from multiple sources to better characterize risk factors of the disease.
We want to investigate different methodologies to face typical challenges in medical datasets. Namely, class-imbalance and considering datasets with a reduced number of annotations. We plan to investigate alternatives to supervised learning.