Medical image segmentation have drawn massive attention as it is important in biomedical
image analysis. Good segmentation results can assist doctors with their judgement and …

Various segmentation networks based on Swin Transformer have shown promise in medical
segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training …

The segmentation of tumours requires accurate identification and localisation in medical
images. With the advent of U-Net and its variants, medical image segmentation has …

Abstract Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been
widely employed in medical image segmentation. While CNNs excel in local feature …

This decade is marked by the introduction of Vision Transformer, a radical paradigm shift in
broad computer vision. A similar trend is followed in medical imaging, UNet, one of the most …

Abstract Convolutional Neural Networks (CNNs), particularly UNet, have become prevalent
in medical image segmentation tasks. However, CNNs inherently struggle to capture global …

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

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

The hybrid architecture of convolution neural networks (CNN) and Transformer has been the
most popular method for medical image segmentation. However, the existing networks …

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 …