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In this paper, we propose a novel approach for few-shot semantic segmentationwith sparse labeled images. We investigate the effectiveness of our method,which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in themedical scenario, where the use of sparse labeling and few-shot can alleviatethe cost of producing new annotated datasets. Our method uses sparse labels inthe meta-training and dense labels in the meta-test, thus making the modellearn to predict dense labels from sparse ones. We conducted experiments withfour Chest X-Ray datasets to evaluate two types of annotations (grid andpoints). The results show that our method is the most suitable when the targetdomain highly differs from source domains, achieving Jaccard scores comparableto dense labels, using less than 2% of the pixels of an image with labels infew-shot scenarios.

Learning to Segment Medical Images from Few-Shot Sparse Labels