Convolutional architectures have demonstrated remarkable success in various vision tasks,
offering efficient learning through their inherent induction bias. However, they might be …

Automated medical image segmentation is a crucial step in clinical analysis and diagnosis,
as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks …

Medical image segmentation is a crucial and intricate process in medical image processing
and analysis. With the advancements in artificial intelligence, deep learning techniques …

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

Medical image segmentation is important for computer-aided diagnosis. Good segmentation
demands the model to see the big picture and fine details simultaneously, ie, to learn image …

Deep learning-based medical image segmentation has made great progress over the past
decades. Scholars have proposed many novel transformer-based segmentation networks to …

Medical image segmentation remains particularly challenging for complex and low-contrast
anatomical structures. In this paper, we introduce the U-Transformer network, which …

Nowadays, convolutional neural networks (CNNs) are widely adopted in medical image
analysis. Nevertheless, the inherent local nature of the convolution operator leads to …

While CNN-based methods have been the cornerstone of medical image segmentation due
to their promising performance and robustness, they suffer from limitations in capturing long …

Medical image segmentation plays a vital role in various clinical applications, enabling
accurate delineation and analysis of anatomical structures or pathological regions …