The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet
discriminative features. Most previous works achieve this by explicitly selecting the …

The increasing popularity of attention mechanisms in deep learning algorithms for computer
vision and natural language processing made these models attractive to other research …

In this paper, a novel attention augmented Wasserstein generative adversarial network (AA-
WGAN) is proposed for fundus retinal vessel segmentation, where a U-shaped network with …

Extracting global and local feature information is still challenging due to the problems of
retinal blood vessel medical images like fuzzy edge features, noise, difficulty in …

Retinal vascular images contain rich vascular geometric features, so the accuracy of retinal
vascular segmentation is of great significance for the diagnosis of various eye diseases. In …

Vessel segmentation in fundus images is a key procedure in the diagnosis of ophthalmic
diseases, which can play a role in assisting doctors in diagnosis. Although current deep …

Segmenting retinal blood vessels from retinal images is a crucial step in ocular disease
diagnosis. It is also one of the most important applications and research in ophthalmic image …

The retinal blood vessel is an essential part of the fundus structure. It is important to
accurately analyze the structure and distribution of retinal vessels, which can help make …

Accurate segmentation of the hepatic vein can improve the precision of liver disease
diagnosis and treatment. Since the hepatic venous system is a small target and sparsely …

The leading cause of vision loss globally is diabetic retinopathy. Researchers are making
great efforts to automatically detect and diagnose correctly diabetic retinopathy. Diabetic …