Medical image segmentation is an important application field of computer vision in medical
image processing. Due to the close location and high similarity of different organs in medical …

Medical image segmentation plays a crucial role in computer-aided diagnosis. However,
due to the large variability of medical images, accurate segmentation is a highly challenging …

How to fuse low-level and high-level features effectively is crucial to improving the accuracy
of medical image segmentation. Most CNN-based segmentation models on this topic usually …

In this paper, we embed two types of attention modules in the dilated fully convolutional
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …

Purpose Medical image segmentation is the most widely used technique in diagnostic and
clinical research. However, accurate segmentation of target organs from blurred border …

The image feature extraction method based on the attention mechanism has contributed
significantly to the accuracy of medical image segmentation. However, the current attention …

As an important building block in automatic medical systems, image segmentation has made
great progress due to the data-driving mechanism of deep architecture. Recently, numerous …

Background Edge accuracy and positional accuracy are the two goals pursued by medical
image segmentation. In clinical medicine diagnosis and research, these two goals enable …

The automatic segmentation of medical images has made continuous progress due to the
development of convolutional neural networks (CNNs) and attention mechanism. However …

Medical images are an important means to assist doctors in making judgments. For the
problem that it is difficult to segment various complex tissue structures in medical images, we …