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

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 …

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

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

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 …

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs)
have been two dominant architectures in medical image segmentation. Although CNNs can …

Automated medical image segmentation is becoming increasingly crucial in modern clinical
practice, driven by the growing demand for precise diagnoses, the push towards …