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 …

A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for
medical image segmentation. Different variants of encoder-decoder networks are in practice …

In the field of computer-aided medical diagnosis, it is crucial to adapt medical image
segmentation to limited computing resources. There is tremendous value in developing …

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

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 …

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

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

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 …

Medical image semantic segmentation is a crucial technique in medical imaging processing,
providing essential diagnostic support by precisely delineating different tissue structures and …

In recent years, UNet and its improved variants have become the main methods for medical
image segmentation. Although these models have achieved excellent results in …