Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. Issue 3 (3rd September 2019)

• Record Type: Journal Article
• Authors: Abels, Esther; Pantanowitz, Liron; Aeffner, Famke; Zarella, Mark D; van der Laak, Jeroen; Bui, Marilyn M; Vemuri, Venkata NP; Parwani, Anil V; Gibbs, Jeff; Agosto‐Arroyo, Emmanuel; Beck, Andrew H; Kozlowski, Cleopatra
• Is Part Of: Journal of pathology. Volume 249:Issue 3(2019)
• Page Start: 286
• Page End: 294
• Publication Date: 2019-09-03
• Subjects: artificial intelligence -- computational pathology -- convolutional neural networks -- digital pathology -- deep learning -- image analysis -- machine learning; Pathology -- Periodicals; 616.07
• DOI: 10.1002/path.5331
• Languages: English
• ISSNs: 0022-3417

Abstract

Abstract: In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.