Cross-Dataset Generalization For Retinal Lesions Segmentation

TL;DR

This paper examines how well retinal lesion segmentation models trained on different datasets generalize across datasets, analyzing various techniques to improve cross-dataset performance and combining coarse and fine labels effectively.

Contribution

It characterizes multiple retinal lesion datasets and compares techniques like stochastic weight averaging, model soups, and ensembles to enhance model generalization across datasets.

Findings

Combining coarse and fine labels improves segmentation accuracy.

Ensemble methods outperform single models in cross-dataset scenarios.

Certain techniques significantly enhance generalization performance.

Abstract

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.