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.

翻译：眼底图像中病变识别是实现视网膜疾病自动化及可解释性诊断的重要里程碑。为支持该方向研究，多个公开数据集提供了不同病变类型的真值标注图。然而，这些标注之间存在显著差异，由此引发跨数据集泛化能力的问题。本研究对多个已知数据集进行特征分析，并对比了若干增强模型泛化性能的技术方案，如随机权重平均、模型汤与集成学习。研究结果揭示了如何将粗粒度标注数据与细粒度数据集相结合以提升病变分割效果的方法论见解。