  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 sparse ones. We conducted experiments with
four Chest X-Ray datasets to evaluate two types of annotations (grid and
points). The results show that our method is the most suitable when the target
domain highly differs from source domains, achieving Jaccard scores comparable
to dense labels, using less than 2% of the pixels of an image with labels in
few-shot scenarios.
