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

By Esther Abels, Liron Pantanowitz, F. Aeffner, M. Zarella, Jeroen Vd Laak, M. Bui, Venkata. N. P. Vemuri, Anil V. Parwani, Jeff Gibbs, Emmanuel Agosto-Arroyo and 2 more. Journal of Pathology, 2019

397 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d0cb89e0f9ebd7cdfe33f355fe48e8fa256dbde7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Neil A. Tenenholtz
2. Rodney Hull
3. F. Francies
4. Zodwa Dlamini
5. R. Marima

In Molecular Biology

1. Rodney Hull
2. F. Francies
3. Zodwa Dlamini
4. R. Marima

Citations in the corpus

• Artificial intelligence (AI) and big data in cancer and precision oncology (2020, background): “In other words, the created pixel pipeline can now become part of a deep learning algorithm to look for patterns, features and shapes that use image analysis, deep learning and AI tools [56].”
• Artificial intelligence for digital and computational pathology (2023, neutral): “8 These authors contributed equally: Andrew H. Song, Guillaume Jaume. e-mail: FaisalMahmood@ bwh.harvard.edu”
• A foundation model for clinical-grade computational pathology and rare cancers detection (2024, positive): “Computational pathology applies artificial intelligence (AI) to digitized WSIs to support the diagnosis, characterization and understanding of disease 5,6 .”