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 …

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 …

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 …

In the process of cancer diagnosis and treatment, accurate extraction of the lesion area
helps the doctor to judge the condition. Currently, medical image segmentation algorithms …

In recent years, medical image segmentation methods based on deep learning have been of
great importance for disease diagnosis and treatment planning in clinical medicine. U-Net …

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 …

Convolution operation is performed within a local window of the input image. Therefore,
convolutional neural network (CNN) is skilled in obtaining local information. Meanwhile, the …

In recent years, precise medical image segmentation methods based on the encoder–
decoder structure have attracted much attention, but there are still some limitations. They …

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 …

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a
substantial diagnostic basis for the physicians to identify the focus lesion in the patient body …