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

Medical image segmentation result is an essential reference for disease diagnosis.
Recently, with the development and application of convolutional neural networks, medical …

Current state-of-the-art medical image segmentation techniques predominantly employ the
encoder–decoder architecture. Despite its widespread use, this U-shaped framework …

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

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 …

Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …

Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …

Medical image segmentation is crucial for the development of computer-aided diagnostic
and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly …

Accurate medical image segmentation is of great significance for subsequent diagnosis and
analysis. The acquisition of multi-scale information plays an important role in segmenting …