The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the
workload in clinical histopathology. Carcinomas consist of malignant epithelial cells …

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …

Digital pathology enables automatic analysis of histopathological sections using artificial
intelligence (AI). Automatic evaluation could improve diagnostic efficiency and help find …

Automatic segmentation of histopathology whole-slide images (WSI) usually involves
supervised training of deep learning models with pixel-level labels to classify each pixel of …

Computational pathology methods have the potential to improve access to precision
medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly …

Detecting cell types from histopathological images is essential for various digital pathology
applications. However, the large number of cells in whole-slide images (WSIs) necessitates …

Pathologists worldwide are facing remarkable challenges with increasing workloads and
lack of time to provide consistently high-quality patient care. The application of artificial …

Simple Summary We present an open-source AI framework for marking, training, and
automated recognition of pathological features in whole-slide scans of diagnostic tissue …

Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a
challenging endeavor due to a lack of standard reference data (ground truth). This topic …

Joint analysis of multiple biomarker images and tissue morphology is important for disease
diagnosis, treatment planning and drug development. It requires cross-staining comparison …