The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

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 …

In this paper, we present T-Net, a fully convolutional net-work particularly well suited for
resource constrained andmobile devices, which cannot cater for the computationalresources …

Segmentation of retinal vessels on fundus images plays a critical role in the diagnosis of
micro-vascular and ophthalmological diseases. Although being extensively studied, this task …

In recent years, convolutional neural network architectures have become increasingly
complex to achieve improved performance on well-known benchmark datasets. In this …

Retinal vessels are essential biomarkers for the early diagnosis of ophthalmic diseases.
Accurate segmentation of retinal vessels is necessary for further quantitative analysis of …

The segmentation of the polyp region in colonoscopy images is considered difficult due to
size, texture, and color variation. To segment polyps successfully, models based on …

Abstract Fundus Image Segmentation (FIS) is an essential procedure for the automated
diagnosis of ophthalmic diseases. Recently, deep fully convolutional networks have been …

Purpose To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal
vessel Topology and siZe) software in detecting retinal features from retinal images captured …

Objectives Although the segmentation of retinal vessels in the fundus is of great significance
for screening and diagnosing retinal vascular diseases, it remains difficult to detect the low …