The major challenges for medical image segmentation tasks are complex backgrounds and
fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation …

Segmentation is an important task in a wide range of computer vision applications, including
medical image analysis. Recent years have seen an increase in the complexity of medical …

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 …

The purpose of medical image segmentation is to identify target organs, tissues or lesion
areas at the pixel level to help doctors evaluate and prevent diseases. Therefore, the model …

Accurate medical image segmentation can assist doctors in observing lesion areas and
making precise judgments. Effectively utilizing important multi-scale semantic information in …

With the rise of deep learning, the U-Net network, based on a U-shaped architecture and
skip connections, has found widespread application in various medical image segmentation …

Residual network (ResNet) and densely connected network (DenseNet) have significantly
improved the training efficiency and performance of deep convolutional neural networks …

Medical image segmentation is crucial for disease diagnosis and monitoring. Though
effective, the current segmentation networks such as UNet struggle with capturing long …

Image segmentation is one of the vital steps in medical image analysis. A large number of
methods based on convolutional neural networks have emerged, which can extract abstract …

Objective. Convolutional neural networks have been widely adopted for medical image
segmentation with their outstanding feature representation capabilities. As the segmentation …