Effective pattern recognition requires carefully designed ground-truth datasets. In this
technical note, we first summarize potential data collection issues in digital pathology and …

An increasing number of machine learning applications are being developed and applied to
digital pathology, including hematopathology. The goal of these modern computerized tools …

Background and objectives Reproducibility is a major challenge in developing machine
learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging …

Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

Deep Learning (DL) approaches have been providing state-of-the-art performance in
different modalities in the field of medical imagining including Digital Pathology Image …

PURPOSE Deep learning (DL), a class of approaches involving self-learned discriminative
features, is increasingly being applied to digital pathology (DP) images for tasks such as …

Motivation Digitization of pathology laboratories through digital slide scanners and
advances in deep learning approaches for objective histological assessment have resulted …

Objectives: Recent progress in automated tissue analysis (tissomics) provides reproducible
phenotypical characterization of histological specimens. We introduce informatics tools to …

We present a computationally efficient method for analyzing H&E stained digital pathology
slides with the objective of discriminating diagnostically relevant vs. irrelevant regions. Such …

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to
digital H&E images, and other histological image types, has begun to radically change how …