Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports
- 1 October 2020
- journal article
- research article
- Published by Elsevier BV in Journal of Biomedical Informatics
- Vol. 110, 103564
- https://doi.org/10.1016/j.jbi.2020.103564
Abstract
No abstract availableFunding Information
- U.S. Department of Energy
- Foundation for the National Institutes of Health
This publication has 35 references indexed in Scilit:
- Convolutional neural net bagging for online visual trackingComputer Vision and Image Understanding, 2016
- Using machine learning to parse breast pathology reportsBreast Cancer Research and Treatment, 2016
- Bagging Tree Classifier and Texture Features for Tumor Identification in Histological ImagesProcedia Computer Science, 2016
- Does diversity improve deep learning?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized votingAustralasian Physics & Engineering Sciences in Medicine, 2015
- Auto-encoder based bagging architecture for sentiment analysisJournal of Visual Languages & Computing, 2014
- A bagging SVM to learn from positive and unlabeled examplesPattern Recognition Letters, 2014
- A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries.2012
- The feasibility of using natural language processing to extract clinical information from breast pathology reportsJournal of Pathology Informatics, 2012
- Bagging predictorsMachine Learning, 1996