Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular subtypes at the same time and provide reliable imaging biomarkers is still challenging.