The success of Transformer in computer vision has attracted increasing attention in the
medical imaging community. Especially for medical image segmentation, many excellent …

The application of deep learning in the medical field has continuously made huge
breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net …

ABSTRACT∗ Transformer completement convolutional neural network (CNN) has achieved
better performance than improved CNN-based methods. Especially, Transformer is utilized …

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 …

Recently, researchers have introduced Transformer into medical image segmentation
networks to encode long-range dependency, which makes up for the deficiencies of …

In medical image segmentation task, convolutional neural networks (CNNs) are difficult to
capture long-range dependencies, but transformers can model the long-range …

Recently, the medical image segmentation have made rapid progress. Specifically, the
precision of medical image segmentation play a pivotal role in the realm of disease …

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

In recent years, Transformers have achieved success in the field of medical image
segmentation due to their outstanding capability to model long-range dependencies …

Like other applications in computer vision, medical image segmentation and his email
address have been most successfully addressed using deep learning models that rely on …