Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Summary Background The Gleason score is the strongest correlating predictor of recurrence
for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for …

The high capacity of neural networks allows fitting models to data with high precision, but
makes generalization to unseen data a challenge. If a domain shift exists, ie differences in …

Understanding the spatial heterogeneity of tumours and its links to disease initiation and
progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily …

Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …

In pathology and biomedical research, histology is the cornerstone method for tissue
analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time …

The application of deep learning for automated segmentation (delineation of boundaries) of
histologic primitives (structures) from whole slide images can facilitate the establishment of …

Image analysis in the field of digital pathology has recently gained increased popularity. The
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …