Accurate segmentation of medical images is essential for clinical decision-making, and deep
learning techniques have shown remarkable results in this area. However, existing …

Accurate medical image segmentation is essential for clinical quantification, disease
diagnosis, treatment planning and many other applications. Both convolution-based and …

Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …

Over the past decade, deep convolutional neural networks have been widely adopted for
medical image segmentation and shown to achieve adequate performance. However, due …

Owing to the various object types and scales, complicated backgrounds, and similar
appearance between tissues in medical images, it is difficult to extract some valuable …

Transformers have shown great success in medical image segmentation. However,
transformers may exhibit a limited generalization ability due to the underlying single-scale …

Medical image segmentation is one of the most fundamental tasks concerning medical
information analysis. Various solutions have been proposed so far, including many deep …

Medical images contain complex information, and the automated analysis of medical images
can greatly assist doctors in clinical decision making. Therefore, the automatic segmentation …

In this paper, we propose a novel network named Vision Transformer for Biomedical Image
Segmentation (ViTBIS). Our network splits the input feature maps into three parts with 1 * 1 …

Accurate segmentation of organs, tissues and lesions is essential for computer-assisted
diagnosis. Previous works have achieved success in the field of automatic segmentation …