Automatic segmentation of the organ's tumor and lesion on biomedical imaging is an
essential initiative towards clinical study, treatment planning and digital biomedical …

Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic
liver and tumor segmentation are of great value in clinical practice as they can reduce …

Compared with the traditional analysis of computed tomography scans, automatic liver tumor
segmentation can supply precise tumor volumes and reduce the inter-observer variability in …

Accurate medical image segmentation is critical for disease quantification and treatment
evaluation. While traditional U-Net architectures and their transformer-integrated variants …

Segmenting medical images is a necessary prerequisite for disease diagnosis and
treatment planning. Among various medical image segmentation tasks, U-Net-based …

In recent years, U-Net and its extended variants have made remarkable progress in the
realm of liver and liver tumor segmentation. However, the limitations of single-path …

Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …

Medical image segmentation plays an important role in many clinical medicines, such as
medical diagnosis and computer-assisted treatment. However, due to the large quality …

As an essential prerequisite for developing a medical intelligent assistant system, medical
image segmentation has received extensive research and concentration from the neural …

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 …