An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Journal: Journal of the National Cancer Institute

Published Date: Sep 1, 2019

Abstract

BACKGROUND: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid application is practical but not reproducible or accurate. The objective of this study was to develop a "deep learning"-based visual evaluation algorithm that automatically recognizes cervical precancer/cancer.

Authors

Liming Hu
David Bell
Sameer Antani

Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Zhiyun Xue

Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Kai Yu
Matthew P Horning
Noni Gachuhi
Benjamin Wilson
Mayoore S Jaiswal
Brian Befano
L Rodney Long

National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Rolando Herrero
Mark H Einstein
Robert D Burk
Maria Demarco
Julia C Gage
Ana Cecilia Rodriguez
Nicolas Wentzensen
Mark Schiffman

Keywords

Adult Aged Aged, 80 and over Algorithms Area Under Curve Case-Control Studies Cervix Uteri Colposcopy Deep Learning Early Detection of Cancer Female Humans Image Processing, Computer-Assisted Mass Screening Middle Aged Population Surveillance Sensitivity and Specificity Severity of Illness Index Uterine Cervical Dysplasia Uterine Cervical Neoplasms Young Adult

External Resources

View on PubMed Access via DOI PubMed (30629194)

An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Abstract

Authors

Keywords

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