The convolutional-based methods provide good segmentation performance in the medical
image segmentation task. However, those methods have the following challenges when …

Accurate segmentation of computerized tomography (CT) images is of great significance to
clinical diagnosis. However, because of the high similarity of gray values, it is a challenging …

The major challenges for medical image segmentation tasks are complex backgrounds and
fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation …

Segmentation of the kidney and kidney tumors using computed tomography (CT) is a crucial
step in related surgical procedures. Although many deep learning models have been …

In this paper, we propose an Outlined Attention U-network (OAU-net) with bypass branching
strategy to solve biomedical image segmentation tasks, which is capable of sensing shallow …

Background With the development of deep learning (DL), more and more methods based on
deep learning are proposed and achieve state-of-the-art performance in biomedical image …

The medical decision-making system is an advanced system for patients that can assist
doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due …

Medical image segmentation-the prerequisite of numerous clinical needs-has been
significantly prospered by recent advances in convolutional neural networks (CNNs) …

Image segmentation is typically used to locate objects and boundaries. It is essential in
many clinical applications, such as the pathological diagnosis of hepatic diseases, surgical …

Osteosarcoma is a primary malignant tumor. It is difficult to cure and expensive to treat.
Generally, diagnosis is made by analyzing MRI images of patients. In the process of clinical …