  Deep-learning-based approaches for retinal lesion segmentation often require
an abundant amount of precise pixel-wise annotated data. However, coarse
annotations such as circles or ellipses for outlining the lesion area can be
six times more efficient than pixel-level annotation. Therefore, this paper
proposes an annotation refinement network to convert a coarse annotation into a
pixel-level segmentation mask. Our main novelty is the application of the
prototype learning paradigm to enhance the generalization ability across
different datasets or types of lesions. We also introduce a prototype weighing
module to handle challenging cases where the lesion is overly small. The
proposed method was trained on the publicly available IDRiD dataset and then
generalized to the public DDR and our real-world private datasets. Experiments
show that our approach substantially improved the initial coarse mask and
outperformed the non-prototypical baseline by a large margin. Moreover, we
demonstrate the usefulness of the prototype weighing module in both
cross-dataset and cross-class settings.
