Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Purpose To perform a systematic review on the research on the application of artificial
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …

Color medical images introduce an additional confounding factor compared to conventional
grayscale medical images: color variability. This variability can lead to inconsistent …

Background and objective Dermatological images are typically diagnosed based on visual
analysis of the skin lesion acquired using a dermoscope. However, the final quality of the …

In kidney transplantations, pathologists evaluate the architecture of both glomeruli,
interstitium and tubules to assess the nephron status. An accurate assessment of …

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a
wide range of applications in image analysis, ranging from automated segmentation to …

Liver image segmentation has been increasingly employed for key medical purposes,
including liver functional assessment, disease diagnosis, and treatment. In this work, we …

Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate
cancer. However, manual three-dimensional (3D) segmentation of the prostate is a …

BACKGROUND Hepatic steatosis is a major cause of chronic liver disease. Two-
dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and …

Abstract Background and Objective: Hepatocellular ballooning is an important histological
parameter in the diagnosis of nonalcoholic steatohepatitis (NASH), and it is considered to be …