Learning to Learn Unlearned Feature for Brain Tumor Segmentation

2023-05-13 · 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.

📄 PDF Abstract BibTeX arXiv:2305.08878

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Active Learning Brain Tumor Segmentation Image Segmentation Medical Image Segmentation Meta-Learning Segmentation Semantic SegmentationTAGTransfer Learning Tumor Segmentation

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