Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer
Authors
Keywords
-
Journal
Nature Communications
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-06-11
DOI
10.1038/s41467-020-16777-6
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience
- (2018) Masashi Misawa et al. GASTROENTEROLOGY
- Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy
- (2018) Gregor Urban et al. GASTROENTEROLOGY
- Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis
- (2018) Peng-Jen Chen et al. GASTROENTEROLOGY
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Focal loss for dense object detection
- (2018) Tsung-Yi Lin et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy
- (2018) Pu Wang et al. Nature Biomedical Engineering
- Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study
- (2018) Xiangchun Li et al. LANCET ONCOLOGY
- Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study
- (2016) Yuichi Mori et al. ENDOSCOPY
- Regional center for complex colonoscopy: yield of neoplasia in patients with prior incomplete colonoscopy
- (2016) Benjamin L. Bick et al. GASTROINTESTINAL ENDOSCOPY
- PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
- (2015) Jan Grau et al. BIOINFORMATICS
- Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps
- (2015) Joep E G IJspeert et al. GUT
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Cancer treatment and survivorship statistics, 2012
- (2012) Rebecca Siegel et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Colorectal-Cancer Incidence and Mortality with Screening Flexible Sigmoidoscopy
- (2012) Robert E. Schoen et al. NEW ENGLAND JOURNAL OF MEDICINE
- Colonoscopic Polypectomy and Long-Term Prevention of Colorectal Cancer Deaths
- (2012) Ann G. Zauber et al. OBSTETRICAL & GYNECOLOGICAL SURVEY
- Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)
- (2011) Yoshito Takemura et al. GASTROINTESTINAL ENDOSCOPY
- Colonic work-up after incomplete colonoscopy: significant new findings during follow-up
- (2010) M. Neerincx et al. ENDOSCOPY
- The Reduction in Colorectal Cancer Mortality After Colonoscopy Varies by Site of the Cancer
- (2010) Harminder Singh et al. GASTROENTEROLOGY
- Once-only flexible sigmoidoscopy screening in prevention of colorectal cancer: a multicentre randomised controlled trial
- (2010) Wendy S Atkin et al. LANCET
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started