Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

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 …

Automatic segmentation of retinal vessels in fundus images plays an important role in the
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …

Accurately segmenting retinal vessel from retinal images is essential for the detection and
diagnosis of many eye diseases. However, it remains a challenging task due to (1) the large …

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes
lesions on the retina that effect vision. If it is not detected early, it can lead to blindness …

The increase of available large clinical and experimental datasets has contributed to a
substantial amount of important contributions in the area of biomedical image analysis …

Retinal vasculature provides an opportunity for direct observation of vessel morphology,
which is linked to multiple clinical conditions. However, objective and quantitative …

Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To
further improve the performance of vessel segmentation, we propose IterNet, a new model …

Background Blood vessel segmentation is a topic of high interest in medical image analysis
since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and …

Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …