Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …

Aleatoric uncertainty captures noise within the observations. For camouflaged object
detection, due to similar appearance of the camouflaged foreground and the background, it's …

In this paper, we propose a novel image segmentation method to tackle two critical problems
of medical image, which are (i) ambiguity of structure boundary in the medical image domain …

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 …

To solve the problems associated with a small sample size during intelligent road detection,
a virtual image set generation method for asphalt pavement cracks is proposed based on …

Though convolutional neural networks are widely used in different tasks, lack of
generalization capability in the absence of sufficient and representative data is one of the …

Generative adversarial networks (GAN) are widely used in medical image analysis tasks,
such as medical image segmentation and synthesis. In these works, adversarial learning is …

Fundus diseases classification is vital for the health of human beings. However, most of
existing methods detect diseases by means of single angle fundus images, which lead to the …

Accurate segmentation of X-ray angiography images is imperative for cardiovascular
disease diagnosis. Despite significant strides in segmentation through deep learning …

Segmentation of organs of interest in 3D medical images is necessary for accurate
diagnosis and longitudinal studies. Though recent advances using deep learning have …