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 …

Objectives Machine learning (ML) for medical imaging is emerging for several organs and
image modalities. Our objectives were to provide clinicians with an overview of this field by …

Liver lesion segmentation is an essential process to assist doctors in hepatocellular
carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography …

Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate
cancer and other prostate diseases. The automatic segmentation of images from prostate …

The liver tumor is one of the most foremost critical causes of death in the world. Nowadays,
Medical Imaging (MI) is one of the prominent Computer Vision fields (CV), which helps …

Deep Learning has indeed been widely used in many fields/areas of medicinal images
classification, and a large number of publications have been published documenting its …

We introduce a neural network framework, utilizing adversarial learning to partition an image
into two cuts, with one cut falling into a reference distribution provided by the user. This …

An estimated 9.6 million deaths, or one in every six deaths, were attributed to cancer in
2018, making it the second highest cause of death worldwide. Men are more likely to …

Background Deep learning segmentation requires large datasets with ground truth. Image
annotation is time consuming and leads to shortages of ground truth data for clinical …

Abdominal organ segmentation is the segregation of a single or multiple abdominal organ
(s) into semantic image segments of pixels identified with homogeneous features such as …