The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …

The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

Abstract Machine learning has been developed dramatically and witnessed a lot of
applications in various fields over the past few years. This boom originated in 2009, when a …

Automated retinal vessel segmentation is among the most significant application and
research topics in ophthalmologic image analysis. Deep learning based retinal vessel …

High-precision segmentation of fundus vessels is a fundamental step in the diagnosis and
treatment of fundus diseases, in which both thick and thin vessels are important features for …

The eye affords a unique opportunity to inspect a rich part of the human microvasculature
non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are …

Retinal vessel segmentation is generally grounded in image-based datasets collected with
bench-top devices. The static images naturally lose the dynamic characteristics of retina …

Accurate pavement crack segmentation is crucial for civil engineering and infrastructure
maintenance. To address the challenge of imbalanced data resulting from the prevalence of …

Purpose To perform one-shot retinal artery and vein segmentation with cross-modality artery-
vein (AV) soft-label pretraining. Design Cross-sectional study. Subjects The study included …

Convolutional neural networks (CNN), especially numerous U-shaped models, have
achieved great progress in retinal vessel segmentation. However, a great quantity of global …