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 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 …

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

In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been
the prevailing technique in the medical image processing era. Specifically, the U-Net model …

Medical image segmentation is an essential prerequisite for developing healthcare systems,
especially for disease diagnosis and treatment planning. On various medical image …

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) have been the de facto standard in a diverse set of
computer vision tasks for many years. Especially, deep neural networks based on seminal …

Recent advances in transformer-based models have drawn attention to exploring these
techniques in medical image segmentation, especially in conjunction with the U-Net model …

Large-scale models pre-trained on large-scale datasets have profoundly advanced the
development of deep learning. However, the state-of-the-art models for medical image …