  Most uses of Meta-Learning in visual recognition are very often applied to
image classification, with a relative lack of works in other tasks {such} as
segmentation and detection. We propose a generic Meta-Learning framework for
few-shot weakly-supervised segmentation in medical imaging domains. We conduct
a comparative analysis of meta-learners from distinct paradigms adapted to
few-shot image segmentation in different sparsely annotated radiological tasks.
The imaging modalities include 2D chest, mammographic and dental X-rays, as
well as 2D slices of volumetric tomography and resonance images. Our
experiments consider a total of 9 meta-learners, 4 backbones and multiple
target organ segmentation tasks. We explore small-data scenarios in radiology
with varying weak annotation styles and densities. Our analysis shows that
metric-based meta-learning approaches achieve better segmentation results in
tasks with smaller domain shifts in comparison to the meta-training datasets,
while some gradient- and fusion-based meta-learners are more generalizable to
larger domain shifts.
