Fig. 1: A high-level overview of our methodology.

We started by testing 3 different architectures on the task of pLGG segmentation (1). The architecture that performed best on this task was used for all ensuing experiments. We then tested whether transfer learning from an adult brain tumor dataset could help improve segmentation performance (2). Next, the segmentation architecture was converted into a classification model by adding some layers and removing others. The classification model was trained and evaluated with semiautomatic whole tumor volume segmentations as input (3) to generate baseline results. Finally, the classification model was trained and tested with whole-brain FLAIR MRIs as input using three different initialization schemes: random (4), transfer learning (5), and pretraining (6).