Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

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

Transformers have recently gained attention in the computer vision domain due to their
ability to model long-range dependencies. However, the self-attention mechanism, which is …

Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of
computer vision tasks for many years. Especially, deep neural networks based on seminal …

Medical image segmentation has seen significant improvements with transformer models,
which excel in gras** far-reaching contexts and global contextual information. However …

Skin lesion segmentation is a challenging task due to the large variation of anatomy across
different cases. In the last few years, deep learning frameworks have shown high …

In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been
the prevailing technique in the medical image processing era. Specifically, the U-Net model …

Convolutional neural networks (CNN)(eg, UNet) have become the de facto standard and
attained immense success in medical image segmentation. However, CNN based methods …

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