U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Liver and liver tumor segmentation from 3D volumetric images has been an active research
area in the medical image processing domain for the last few decades. The existence of …

Automatic assessing the location and extent of liver and liver tumor is critical for radiologists,
diagnosis and the clinical process. In recent years, a large number of variants of U-Net …

Dental caries has been a common health issue throughout the world, which can even lead
to dental pulp and root apical inflammation eventually. Timely and effective treatment of …

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical
applications, such as postoperative assessment, surgical planning, and pathological …

White blood cells (WBCs) are a portion of the immune system which fights against germs.
Leukemia is the most common blood cancer which may lead to death. It occurs due to the …

Breast ultrasound medical images are characterized by poor imaging quality and irregular
target edges. During the diagnosis process, it is difficult for physicians to segment tumors …

Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer.
Segmentation networks extract features continuously at the same scale, which cannot adapt …

Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic
liver and tumor segmentation are of great value in clinical practice as they can reduce …

Although the existing deep supervised solutions have achieved some great successes in
medical image segmentation, they have the following shortcomings;(i) semantic difference …