Recent advancements in medical image segmentation have demonstrated superior
performance by combining Transformer and U-Net due to the Transformer's exceptional …

Recent advances in transformer-based models have drawn attention to exploring these
techniques in medical image segmentation, especially in conjunction with the U-Net model …

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

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

Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

Due to the scarcity and specific imaging characteristics in medical images, light-weighting
Vision Transformers (ViTs) for efficient medical image segmentation is a significant …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …

Medical image segmentation plays a crucial role in advancing healthcare systems for
disease diagnosis and treatment planning. The u-shaped architecture, popularly known as …