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 …

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 …

Convolutional Neural Networks (CNNs) and Transformers have been the most popular
architectures for biomedical image segmentation, but both of them have limited ability to …

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

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 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 …

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 …