Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …

Medical image segmentation has seen significant improvements with transformer models,
which excel in gras** far-reaching contexts and global contextual information. However …

Background: Thanks to the rapid development of computer-based systems and deep-
learning-based algorithms, artificial intelligence (AI) has long been integrated into the …

Background Recent advances in deep learning techniques have led to improved diagnostic
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of
computer vision tasks for many years. Especially, deep neural networks based on seminal …

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent
years, which has good applications in medical image analysis. In this paper, DenseNet is …

In recent years, deep learning technology has shown superior performance in different fields
of medical image analysis. Some deep learning architectures have been proposed and …