  Identifying lesions in fundus images is an important milestone toward an
automated and interpretable diagnosis of retinal diseases. To support research
in this direction, multiple datasets have been released, proposing groundtruth
maps for different lesions. However, important discrepancies exist between the
annotations and raise the question of generalization across datasets. This
study characterizes several known datasets and compares different techniques
that have been proposed to enhance the generalisation performance of a model,
such as stochastic weight averaging, model soups and ensembles. Our results
provide insights into how to combine coarsely labelled data with a
finely-grained dataset in order to improve the lesions segmentation.
