Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

The U-Net architecture, built upon the fully convolutional network, has proven to be effective
in biomedical image segmentation. However, U-Net applies skip connections to merge …

Data augmentation is a commonly used technique for increasing both the size and the
diversity of labeled training sets by leveraging input transformations that preserve …

Pneumonia is the leading cause of death among young children and one of the top mortality
causes worldwide. The pneumonia detection is usually performed through examine of chest …

Adequate blood supply is critical for normal brain function. Brain vasculature dysfunctions,
including stalled blood flow in cerebral capillaries, are associated with cognitive decline and …

The human gastrointestinal (GI) tract is an important part of the body. According to World
Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In …

Automatic surgical instrument segmentation is a necessary step for the steady operation of
surgical robots, and the segmentation accuracy directly affects the surgical effect …

Abstract Machine intelligence based automation plays a significant role in many modern
applications, and vision based understanding is a significant element of this. To meet the …

Medical imaging segmentation is an essential technique for modern medical applications. It
is the foundation of many aspects of clinical diagnosis, oncology and computer-integrated …

Precise medical image segmentation is a crucial step for proper isolation of target regions,
such as an organ or lesion for accurate medical diagnosis, prognosis and certain medical …