Accurate segmentation and lesion localization are essential for treating diseases in medical
images. Despite deep learning methods enhancing segmentation, they still have limitations …

Accurate medical image segmentation is critical for disease quantification and treatment
evaluation. While traditional U-Net architectures and their transformer-integrated variants …

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 …

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

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

Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation.
However, medical images have complex shapes, the structures of different objects are very …

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

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 …

Automatic medical image segmentation as assistance to doctors is important for diagnosis
and treatment of various diseases. TransUNet that integrates the advantages of transformer …

In the last decade, deep neural networks have been widely applied to medical image
segmentation, achieving good results in computer-aided diagnosis tasks etc. However, the …