As a result of the rise of Transformer architectures in medical image analysis, specifically in
the domain of medical image segmentation, a multitude of hybrid models have been created …

Convolutional neural networks have gained tremendous success in computer vision and
medical imaging applications. To make these models truly portable and compatible for …

Recently, convolutional neural network (CNN) has achieved great success in medical image
segmentation. However, due to the limitation of convolutional receptive field, the pure …

The automated segmentation of cancer tissue in histopathology images can help clinicians
to detect, diagnose, and analyze such disease. Different from other natural images used in …

Convolutional neural networks (CNN), especially U-shaped networks, have become the
mainstream approach for medical image segmentation. However, due to the intrinsic locality …

As the most successful network structure in biomedical image segmentations, U-Net has
presented excellent performance in many medical image segmentation tasks. We argue that …

There are already many methods based on U-net, however, due to the paricularity of
medical images, we need to pay more attention to the target area to perform more detailed …

Background: Accurate semantic segmentation of kidney tumors in computed tomography
(CT) images is difficult because tumors feature varied forms and occasionally, look alike …

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and
adolescent populations. The morphology and size of osteosarcoma MRI images often show …

Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …