Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Simple Summary Technological and scientific innovations, from genetic sequencing to
digital pathology slide scanners, have drastically altered the field of neuropathology. The …

As in other domains, artificial intelligence is becoming increasingly important in medicine. In
particular, deep learning-based pattern recognition methods can advance the field of …

Pathology continues to rapidly evolve as a specialty and solidify its central role in
interdisciplinary medicine. The past decade has witnessed the impact of molecular …

Training machine learning models for artificial intelligence (AI) applications in pathology
often requires extensive annotation by human experts, but there is little guidance on the …

Recently in the field of digital pathology, there have been promising advances with regards
to deep learning for pathological images. These methods are often considered “black …

In the last decade, the field of computational pathology has advanced at a rapid pace
because of the availability of deep neural networks, which achieved their first successes in …

Computational Pathology (CPath) is an emerging field concerned with the study of tissue
pathology via computational algorithms for the processing and analysis of digitized high …

Applications of artificial intelligence and particularly deep learning to aid pathologists in
carrying out laborious and qualitative tasks in histopathologic image analysis have now …

Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to
leverage the enormous potential of personalised medicine and of stratifying patients …