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 …

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 …

In clinical practice, medical image segmentation provides useful information on the contours
and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and …

Image segmentation holds a vital position in the realms of diagnosis and treatment within the
medical domain. Traditional convolutional neural networks (CNNs) and Transformer models …

The recent developments of foundation models in computer vision, especially the Segment
Anything Model (SAM), allow scalable and domain-agnostic image segmentation to serve as …

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 this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical
image segmentation. It aims to augment the infusion of semantic information into low-level …

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 …

In the field of medical image segmentation, models based on both CNN and Transformer
have been thoroughly investigated. However, CNNs have limited modeling capabilities for …

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