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 …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …

Machine vision, an interdisciplinary field that aims to replicate human visual perception in
computers, has experienced rapid progress and significant contributions. This paper traces …

Image segmentation, a crucial process of dividing images into distinct parts or objects, has
witnessed remarkable advancements with the emergence of deep learning (DL) techniques …

Popular semi-supervised medical image segmentation networks often suffer from error
supervision from unlabeled data since they usually use consistency learning under different …

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis
comprises interdependent subtasks such as segmentation, detection and recognition, which …

Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …