As the most successful network structure in biomedical image segmentations, U-Net has
presented excellent performance in many medical image segmentation tasks. We argue that …

U-Net has achieved huge success in various medical image segmentation challenges.
Kinds of new architectures with bells and whistles might succeed in certain dataset when …

Kidney cancer is one of the most common cancers. Precise delineation and localization of
the lesion area play a crucial role in the diagnosis and treatment of kidney cancer. Deep …

Background: In the series of improved versions of U-Net, while the segmentation accuracy
continues to improve, the number of parameters does not change, which makes the …

Kidney and Kidney tumor segmentation from CT scans has tremendous potential to help
doctors in early diagnosis and localization of tumor, its size and type and for making timely …

Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder
architecture. However, this U-shaped framework still has limitations in capturing the non …

Background: The major challenge in medical imaging is to achieve high accuracy output
during semantic image segmentation tasks in biomedical imaging while having fewer …

Kidney cancer is one of the common cancers in the world. Automatic segmentation of the
kidney and kidney tumor from CT images is of great significance for the therapy treatment of …

In recent years, both convolutional neural networks (CNN) and transformers have
demonstrated impressive feature extraction capabilities in the field of medical image …

Early intervention in tumors can greatly improve human survival rates. With the development
of deep learning technology, automatic image segmentation has taken a prominent role in …