  High-quality labeled data is essential to successfully train supervised
machine learning models. Although a large amount of unlabeled data is present
in the medical domain, labeling poses a major challenge: medical professionals
who can expertly label the data are a scarce and expensive resource. Making
matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is
tedious and suffers from high inter-rater variance, thus dramatically limiting
available training data. We propose a recursive training strategy to perform
the task of semantic segmentation given only very few training samples with
pixel-level annotations. We expand on this small training set having cheaper
image-level annotations using a recursive training strategy. We apply this
technique on the segmentation of intracranial hemorrhage (ICH) in CT (computed
tomography) scans of the brain, where typically few annotated data is
available.
