UNet and its latest extensions like TransUNet have been the leading medical image
segmentation methods in recent years. However, these networks cannot be effectively …

The u-shaped architecture has emerged as a crucial paradigm in the design of medical
image segmentation networks. However, due to the inherent local limitations of convolution …

The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical
image analysis with a vast number of new methods being proposed each year. This ever …

Large-scale models pre-trained on large-scale datasets have profoundly advanced the
development of deep learning. However, the state-of-the-art models for medical image …

UNet and its variations achieve state-of-the-art performances in medical image
segmentation. In end-to-end learning, the training with high-resolution medical images …

Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …

Deep learning is one of the most effective approaches to medical image processing
applications. Network models are being studied more and more for medical image …

U-Net is a widely adopted neural network in the domain of medical image segmentation.
Despite its quick embracement by the medical imaging community, its performance suffers …