Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning
and monitoring. Traditional methods often encounter challenges due to the complexity and …

Breast cancer has become one of the most dreaded diseases that can threaten the life of
any woman. Accurate target lesion segmentation is essential for early clinical intervention …

Clinical research on smart health has an increasing demand for intelligent and clinic-
oriented medical image computing algorithms and platforms that support various …

With the rapid advancements of big data and computer vision, many large-scale natural
visual datasets are proposed, such as ImageNet-21K, LAION-400M, and LAION-2B. These …

Although the U-shape networks have achieved remarkable performances in many medical
image segmentation tasks, they rarely model the sequential relationship of hierarchical …

A brain tumor puts one's life in danger and interferes with the body's regular operations.
Early identification and treatment planning are essential for a correct diagnosis of a brain …

Due to the various appearance of the polyps and the tiny contrast between the polyp area
and its surrounding background, accurate polyp segmentation has become a challenging …

For multi-modal magnetic resonance (MR) brain tumor segmentation, current methods
usually directly extract the discriminative features from input images for tumor sub-region …

Automatic segmentation of gastric cancer (GC) in CT imaging holds significant clinical
diagnostic and research implications. Despite the advancement of deep learning in medical …

Breast cancer is one of the common cancers that endanger the health of women globally.
Accurate target lesion segmentation is essential for early clinical intervention and …