Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy
- 28 February 2020
- journal article
- research article
- Published by Elsevier BV in Medical Image Analysis
- Vol. 62, 101653
- https://doi.org/10.1016/j.media.2020.101653
Abstract
No abstract availableKeywords
This publication has 21 references indexed in Scilit:
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Deep learningNature Methods, 2015
- Deep learningNature, 2015
- Endoscopic prediction of deep submucosal invasive carcinoma: validation of the Narrow-Band Imaging International Colorectal Endoscopic (NICE) classificationGastrointestinal Endoscopy, 2013
- Narrow‐band imaging with dual focus magnification in differentiating colorectal neoplasiaDigestive Endoscopy, 2013
- Validation of a Simple Classification System for Endoscopic Diagnosis of Small Colorectal Polyps Using Narrow-Band ImagingGastroenterology, 2012
- Active LearningSynthesis Lectures on Artificial Intelligence and Machine Learning, 2012
- Calibrating predictive model estimates to support personalized medicineJournal of the American Medical Informatics Association, 2012
- Screening and Surveillance for the Early Detection of Colorectal Cancer and Adenomatous Polyps, 2008: A Joint Guideline From the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of RadiologyGastroenterology, 2008
- Bayesian parameter estimation via variational methodsStatistics and Computing, 2000