In recent advancements in medical image analysis, Convolutional Neural Networks (CNN)
and Vision Transformers (ViT) have set significant benchmarks. While the former excels in …

Accurate medical image segmentation demands the integration of multi-scale information,
spanning from local features to global dependencies. However, it is challenging for existing …

In the realm of medical image segmentation, both CNN-based and Transformer-based
models have been extensively explored. However, CNNs exhibit limitations in long-range …

Medical image segmentation plays an essential role in developing computer-assisted
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …

The U-shaped architecture has emerged as a crucial paradigm in the design of medical
image segmentation networks. However, due to the inherent local limitations of convolution …

UNet and its variants have been widely used in medical image segmentation. However,
these models, especially those based on Transformer architectures, pose challenges due to …

Medical image segmentation is essential in diagnostics, treatment planning, and healthcare,
with deep learning offering promising advancements. Notably, Convolutional Neural …

Convolutional neural networks (CNN)(eg, UNet) have become the de facto standard and
attained immense success in medical image segmentation. However, CNN based methods …

Medical image segmentation plays a crucial role in advancing healthcare systems for
disease diagnosis and treatment planning. The u-shaped architecture, popularly known as …

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