Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

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 …

In the past few years, convolutional neural networks (CNNs) have achieved milestones in
medical image analysis. In particular, deep neural networks based on U-shaped architecture …

Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications …

We propose SAMed, a general solution for medical image segmentation. Different from the
previous methods, SAMed is built upon the large-scale image segmentation model …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Automatic assessing the location and extent of liver and liver tumor is critical for radiologists,
diagnosis and the clinical process. In recent years, a large number of variants of U-Net …

Abstract Motivation Antimicrobial peptides (AMPs) are essential components of therapeutic
peptides for innate immunity. Researchers have developed several computational methods …

Liver and liver tumor segmentation from 3D volumetric images has been an active research
area in the medical image processing domain for the last few decades. The existence of …

Medical image segmentation plays an essential role in developing computer-assisted
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …