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.
@article{arxiv.2305.08878,
title = {Learning to Learn Unlearned Feature for Brain Tumor Segmentation},
author = {Seungyub Han and Yeongmo Kim and Seokhyeon Ha and Jungwoo Lee and Seunghong Choi},
journal= {arXiv preprint arXiv:2305.08878},
year = {2023}
}