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Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports.

Authors
  • Yoon, Hong-Jun1
  • Klasky, Hilda B2
  • Gounley, John P3
  • Alawad, Mohammed4
  • Gao, Shang5
  • Durbin, Eric B6
  • Wu, Xiao-Cheng7
  • Stroup, Antoinette8
  • Doherty, Jennifer9
  • Coyle, Linda10
  • Penberthy, Lynne11
  • Blair Christian, J12
  • Tourassi, Georgia D13
  • 1 Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
  • 2 Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
  • 3 Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
  • 4 Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
  • 5 Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
  • 6 College of Medicine, University of Kentucky, Lexington, KY 40536, United States of America. Electronic address: [email protected] , (United States)
  • 7 Louisiana Tumor Registry, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA 70112, United States of America. Electronic address: [email protected] , (United States)
  • 8 New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, 08901, United States of America. Electronic address: [email protected] , (Jersey)
  • 9 Utah Cancer Registry, University of Utah School of Medicine, Salt Lake City, UT 84132, United States of America. Electronic address: [email protected] , (United States)
  • 10 Information Management Services Inc., Calverton, MD 20705, United States of America. Electronic address: [email protected] , (United States)
  • 11 Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20814, United States of America. Electronic address: [email protected] , (United States)
  • 12 Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
  • 13 National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States of America. Electronic address: [email protected] , (United States)
Type
Published Article
Journal
Journal of Biomedical Informatics
Publisher
Elsevier
Publication Date
Oct 01, 2020
Volume
110
Pages
103564–103564
Identifiers
DOI: 10.1016/j.jbi.2020.103564
PMID: 32919043
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources and training time. The research question that we aimed to answer in this research is whether we could achieve higher task performance scores and accelerate the training by dividing a problem into sub-problems. The data used in this study consist of free text from electronic cancer pathology reports. We applied bagging and partitioned data training using Multi-Task Convolutional Neural Network (MT-CNN) and Multi-Task Hierarchical Convolutional Attention Network (MT-HCAN) classifiers. We split a big problem into 20 sub-problems, resampled the training cases 2,000 times, and trained the deep learning model for each bootstrap sample and each sub-problem-thus, generating up to 40,000 models. We performed the training of many models concurrently in a high-performance computing environment at Oak Ridge National Laboratory (ORNL). We demonstrated that aggregation of the models improves task performance compared with the single-model approach, which is consistent with other research studies; and we demonstrated that the two proposed partitioned bagging methods achieved higher classification accuracy scores on four tasks. Notably, the improvements were significant for the extraction of cancer histology data, which had more than 500 class labels in the task; these results show that data partition may alleviate the complexity of the task. On the contrary, the methods did not achieve superior scores for the tasks of site and subsite classification. Intrinsically, since data partitioning was based on the primary cancer site, the accuracy depended on the determination of the partitions, which needs further investigation and improvement. Results in this research demonstrate that 1. The data partitioning and bagging strategy achieved higher performance scores. 2. We achieved faster training leveraged by the high-performance Summit supercomputer at ORNL. Copyright © 2020 Elsevier Inc. All rights reserved.

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