TY - JOUR
T1 - Computational pathology definitions, best practices, and recommendations for regulatory guidance
T2 - a white paper from the Digital Pathology Association
AU - Abels, Esther
AU - Pantanowitz, Liron
AU - Aeffner, Famke
AU - Zarella, Mark D.
AU - van der Laak, Jeroen
AU - Bui, Marilyn M.
AU - Vemuri, Venkata N.P.
AU - Parwani, Anil V.
AU - Gibbs, Jeff
AU - Agosto-Arroyo, Emmanuel
AU - Beck, Andrew H.
AU - Kozlowski, Cleopatra
N1 - Publisher Copyright:
© 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - computational pathology
KW - convolutional neural networks
KW - deep learning
KW - digital pathology
KW - image analysis
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85070884507&partnerID=8YFLogxK
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U2 - 10.1002/path.5331
DO - 10.1002/path.5331
M3 - Review article
C2 - 31355445
AN - SCOPUS:85070884507
SN - 0022-3417
VL - 249
SP - 286
EP - 294
JO - Journal of Pathology
JF - Journal of Pathology
IS - 3
ER -