This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Background Over the past decade, the development of molecular high-throughput methods
(omics) increased rapidly and provided new insights for cancer research. In parallel, deep …

Deep learning (DL) is a powerful methodology for the recognition and classification of tissue
structures in digital pathology. Its performance in prostate cancer pathology is still under …

Importance There is a lack of studies exploring the performance of a deep learning survival
neural network in non–small cell lung cancer (NSCLC). Objectives To compare the …

Abstract Purpose of Review Our goal is to provide an overview of machine learning methods
and artificial intelligence in digital pathology image analysis. We also highlight novel …

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in
Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on …

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is
crucial for clinical decision-making, but traditional pathology review is labor intensive and …

In the last decade, the focus of computational pathology research community has shifted
from replicating the pathological examination for diagnosis done by pathologists to …

Since the introduction of the hematoxylin and eosin (H&E)-stained slide in 1876 as the
primary tool in the pathologist's toolbox up until the last decade, few tools, other than the …

In urological medicine, the application of novel artificial intelligence technologies,
particularly ANNs, have been considered to be a promising step in improving physicians' …