Medical image segmentation has been widely studied, and many methods have been
proposed. Among the existing methods, U-Net and its variants have achieved a promising …

Automatic and accurate medical image segmentation is a crucial step for clinical diagnosis
and treatment planning of diseases. The advanced convolutional neural network (CNN) …

Deep learning shows excellent performance in medical image segmentation. However, a
pooling operation in its encoding stage leads to feature loss and the ability of multi-scale …

Medical image segmentation and classification are fundamental tasks in computer-aided
diagnosis, where accurate segmentation plays a key role in identifying disease-related …

Medical image segmentation is a fundamental and challenge task in many computer-aided
diagnosis and surgery systems, and attracts numerous research attention in computer vision …

U-Net has a good representation learning capability in medical image segmentation
because it can extract contextual information from an image. However, U-Net also has two …

In recent years, deep learning achieves significant advancements in medical image
segmentation. Research finds that integrating Transformers and CNNs effectively addresses …

Medical image segmentation result is an essential reference for disease diagnosis.
Recently, with the development and application of convolutional neural networks, medical …

The automatic segmentation of medical images has made continuous progress due to the
development of convolutional neural networks (CNNs) and attention mechanism. However …

Accurate image segmentation plays a crucial role in the development of computer-aided
diagnosis. The U-Net architecture based on Convolutional Neural Network (CNN) is widely …