Accurate and automatic segmentation of medical images is a key step in clinical diagnosis
and analysis. Currently, the successful application of Transformers' model in the field of …

Automated medical image segmentation is a crucial step in clinical analysis and diagnosis,
as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks …

Accurate segmentation of lesions in medical images is a key step to assist clinicians in
diagnosis and analysis. Most studies combine the Transformer model with CNN at a single …

Medical image segmentation is a crucial and intricate process in medical image processing
and analysis. With the advancements in artificial intelligence, deep learning techniques …

In the last few years, Transformer has revolutionized the area of medical image
segmentation. Several similar studies have used the UNet architecture to combine …

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

The accurate segmentation of medical images is critical for various healthcare applications.
Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) like …

Combining transformers and convolutional neural networks is considered one of the most
important directions for tackling medical image segmentation problems. To learn the long …

Accurate segmentation of medical images is essential for clinical decision-making, and deep
learning techniques have shown remarkable results in this area. However, existing …

Medical image segmentation is a crucial step in developing medical systems, especially for
assisting doctors in diagnosing and treating diseases. Currently, UNet has become the …