META-TRANSFER LEARNING FOR FEW-SHOT MENINGIOMA SEGMENTATION

Many algorithms have been developed for brain tumor segmentation over the past years, especially since the inception of the BraTS challenge. However, these models mainly focus on glioma segmentation because of their relatively high incidence. Their performance may not hold for other types of brain tumors, such as meningioma, without a large number of samples to re-train or fine-tune the models. In this work, we propose a new meta-transfer learning network for few-shot meningioma segmentation that combines meta-learning and transfer learning. The proposed meta-transfer learning framework learns shared common knowledge using a large amount of data from more easily accessible glioma data, and then adapts quickly to meningiomas with few-shot cases. We show that our meta-transfer learning gains a respective 29.88% and 5.63% increase in Dice score over few-shot transfer learning and few-shot meta-learning, respectively; and achieves comparable performance against its fully-supervised counterpart while only requiring 2% of its training data.