Although U-Net and its variants have achieved some great successes in medical image
segmentation tasks, their segmentation performances for small objects are still …

abstract Although U-Net and its variants have achieved some great successes in medical
image segmentation tasks, their segmentation performances for small objects are still …

Although the existing deep supervised solutions have achieved some great successes in
medical image segmentation, they have the following shortcomings;(i) semantic difference …

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 is critical for disease quantification and treatment
evaluation. While traditional U-Net architectures and their transformer-integrated variants …

Medical image segmentation plays an important role in many clinical medicines, such as
medical diagnosis and computer-assisted treatment. However, due to the large quality …

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

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 an important and complex task in clinical practices, but the
widely used U-Net usually cannot achieve satisfactory performances in some clinical …