Learning to Learn Unlearned Feature for Brain Tumor Segmentation
Seungyub Han Yeongmo Kim Seokhyeon Ha Jungwoo Lee Seunghong Choi
We propose a fine-tuning algorithm for brain tumor segmentation that needs
only a few data samples and helps networks not to forget the original tasks.
Our approach is based on active learning and meta-learning. One of the
difficulties in medical image segmentation is the lack of datasets with proper
annotations, because it requires doctors to tag reliable annotation and there
are many variants of a disease, such as glioma and brain metastasis, which are
the different types of brain tumor and have different structural features in MR
images. Therefore, it is impossible to produce the large-scale medical image
datasets for all types of diseases. In this paper, we show a transfer learning
method from high grade glioma to brain metastasis, and demonstrate that the
proposed algorithm achieves balanced parameters for both glioma and brain
metastasis domains within a few steps.