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 …

Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …

In semi-supervised medical image segmentation, most previous works draw on the common
assumption that higher entropy means higher uncertainty. In this paper, we investigate a …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Nuclei segmentation is a vital step for pathological cancer research. It is still an open
problem due to some difficulties, such as color inconsistency introduced by non-uniform …

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed
for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic …

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care
system replete with enhanced precision and computing capabilities. Medical imaging …

Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in
extracting knowledge from unlabeled data to assist learning from labeled data. This paper …

The state-of-the-art deep learning methods have demonstrated impressive performance in
segmentation tasks. However, the success of these methods depends on a large amount of …