Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

Esther Abels, Liron Pantanowitz, Famke Aeffner, Mark D. Zarella, Jeroen van der Laak, Marilyn M. Bui, Venkata N.P. Vemuri, Anil V. Parwani, Jeff Gibbs, Emmanuel Agosto-Arroyo, Andrew H. Beck, Cleopatra Kozlowski

Research output: Contribution to journalReview articlepeer-review

45 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)286-294
Number of pages9
JournalJournal of Pathology
Volume249
Issue number3
DOIs
StatePublished - Nov 1 2019
Externally publishedYes

Keywords

  • artificial intelligence
  • computational pathology
  • convolutional neural networks
  • deep learning
  • digital pathology
  • image analysis
  • machine learning

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

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