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 …

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

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 …

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

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

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

Automated medical image segmentation can assist doctors in diagnosing faster and more
accurately. Deep learningbased medical image segmentation models have significantly …

In the field of medical image segmentation, variant models based on Convolutional Neural
Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very …

Though U-Net has achieved tremendous success in medical image segmentation tasks, it
lacks the ability to explicitly model long-range dependencies. Therefore, Vision …