Deep learning has shown great potential for automated medical image segmentation to
improve the precision and speed of disease diagnostics. However, the task presents …

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

Deep learning has successfully been leveraged for medical image segmentation. It employs
convolutional neural networks (CNN) to learn distinctive image features from a defined pixel …

Convolutional neural network (CNN) based methods have achieved great successes in
medical image segmentation, but their capability to learn global representations is still …

Convolutional neural networks (CNN)(eg, UNet) have become the de facto standard and
attained immense success in medical image segmentation. However, CNN based methods …

Hybrid architectures based on Convolutional Neural Networks (CNN) and Transformers
have been extensively employed in medical image segmentation. However, previous …

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

Although various methods based on convolutional neural networks have improved the
performance of biomedical image segmentation to meet the precision requirements of …

Medical image segmentation is crucial for the development of computer-aided diagnostic
and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly …