Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering
Reference16 articles.
1. Chan S, Siegel EL (2019) Will machine learning end the viability of radiology as a thriving medical specialty? Br J Radiol 92:20180416. https://doi.org/10.1259/bjr.20180416
2. Chen C, Liaw A, Brieman L (2004) Using random forest to learn imbalanced data: Technical Report No. 666. University of California, Berkley. Using Random Forest to Learn Imbalanced Data
3. Barandela R, Sánchez JS, García V, Rangel E (2003) Strategies for learning in class imbalance problems. Pattern Recognit 36:849–851. https://doi.org/10.1016/S0031-3203(02)00257-1
4. Klement W, Wilk S, Michalowski W, Matwin S (2011) Classifying severely imbalanced data
5. Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, Chepelev L, Cairns R, Mitchell JR, Cicero MD, Poudrette MG, Jaremko JL, Reinhold C, Gallix B, Gray B, Geis R, O’Connell T, Babyn P, Koff D, Ferguson D, Derkatch S, Bilbily A, Shabana W (2018) Canadian association of radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 69:120–135
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