Medical image segmentation is crucial for accurate diagnosis and treatment in the medical
field. In recent years, convolutional neural networks (CNNs) and Transformers have been …

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

Over the past decade, deep convolutional neural networks have been widely adopted for
medical image segmentation and shown to achieve adequate performance. However, due …

The accurate segmentation of medical images is critical for various healthcare applications.
Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) like …

Medical image segmentation is a crucial and intricate process in medical image processing
and analysis. With the advancements in artificial intelligence, deep learning techniques …

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs)
have been two dominant architectures in medical image segmentation. Although CNNs can …

Convolutional architectures have demonstrated remarkable success in various vision tasks,
offering efficient learning through their inherent induction bias. However, they might be …

In medical image segmentation task, convolutional neural networks (CNNs) are difficult to
capture long-range dependencies, but transformers can model the long-range …

Medical image segmentation is a critical step in many imaging applications. Automatic
segmentation has gained extensive concern using a convolutional neural network (CNN) …

Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …