As a result of the rise of Transformer architectures in medical image analysis, specifically in
the domain of medical image segmentation, a multitude of hybrid models have been created …

Accurate and robust medical image segmentation is crucial for assisting disease diagnosis,
making treatment plan, and monitoring disease progression. Adaptive to different scale …

Recent advancements in medical image segmentation have demonstrated superior
performance by combining Transformer and U-Net due to the Transformer's exceptional …

Due to the scarcity and specific imaging characteristics in medical images, light-weighting
Vision Transformers (ViTs) for efficient medical image segmentation is a significant …

Convolutional neural networks (CNNs) and Transformer-based models are being widely
applied in medical image segmentation thanks to their ability to extract high-level features …

In recent years, both convolutional neural networks (CNN) and transformers have
demonstrated impressive feature extraction capabilities in the field of medical image …

Current medical image segmentation approaches have limitations in deeply exploring multi-
scale information and effectively combining local detail textures with global contextual …

Recent works have shown that the computational efficiency of 3D medical image (eg CT and
MRI) segmentation can be impressively improved by dynamic inference based on slice-wise …

Abstract Convolutional Neural Networks (CNNs) have become widely adopted for medical
image segmentation tasks, demonstrating promising performance. However, the inherent …

In the past decades, deep neural networks, particularly convolutional neural networks, have
achieved state-of-the-art performance in a variety of medical image segmentation tasks …