Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and
the third-leading cause of cancer-related death worldwide, with incidence and mortality rates …

Artificial intelligence has aided in the advancement of healthcare research. The availability
of open-source healthcare statistics has prompted researchers to create applications that aid …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …

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 …

A common shortfall of supervised deep learning for medical imaging is the lack of labeled
data, which is often expensive and time consuming to collect. This article presents a new …

In recent years, significant progress has been made in developing more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …

The relatively recent reintroduction of deep learning has been a revolutionary force in the
interpretation of diagnostic imaging studies. However, the technology used to acquire those …

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 …