Learning to Segment Medical Images from Few-Shot Sparse Labels

@article{Gama2021LearningTS,
  title={Learning to Segment Medical Images from Few-Shot Sparse Labels},
  author={Pedro H. T. Gama and Hugo Neves de Oliveira and Jefersson Alex dos Santos},
  journal={2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},
  year={2021},
  pages={89-96}
}
In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from… 

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