Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology.

Journal: Toxicologic pathology
Published Date:

Abstract

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

Authors

  • Famke Aeffner
    Amgen Research, Comparative Biology and Safety Sciences, Amgen Inc., South San Francisco, CA, USA.
  • Tobias Sing
    Novartis, Novartis Institutes for Biomedical Research, NIBR Informatics, Basel, Switzerland.
  • Oliver C Turner
    Novartis, Novartis Institutes for Biomedical Research, Preclinical Safety, East Hanover, NJ, USA.