Deep ensemble learning for accurate retinal vessel segmentation. (May 2023)

• Record Type: Journal Article
• Authors: Du, Lingling; Liu, Hanruo; Zhang, Lan; Lu, Yao; Li, Mengyao; Hu, Yang; Zhang, Yi
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106829
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Significant progress has been made in deep learning-based retinal vessel segmentation in recent years. However, the current methods suffer from low performance and the robust of the models is not that good. Our work introduces an novel framework for retinal vessel segmentation based on deep ensemble learning. The results of benchmarking comparisons indicate that our model outperforms the existing ones on multiple datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. It evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision Transformer and FCN-Transformer. We expect our proposed method can benefit and accelerate the development of accurate retinal vessel segmentation in this field. Highlights: In this work, we propose a deep ensemble learning framework for the retinal vessel segmentation. Our models outperform the existing approaches on several datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. Our model is capable of capturing the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models.