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

Hong Jun Yoon, Hilda B. Klasky, John P. Gounley, Mohammed Alawad, Shang Gao, Eric B. Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Linda Coyle, Lynne Penberthy, J. Blair Christian, Georgia D. Tourassi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Objective: 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. Materials and Methods:: 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). Results: 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. Conclusion: 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.

Original languageEnglish
Article number103564
JournalJournal of Biomedical Informatics
Volume110
DOIs
StatePublished - Oct 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Inc.

Keywords

  • Bootstrap aggregation
  • Convolutional neural networks
  • Data partitioning
  • Deep learning
  • Hierarchical self-attention networks
  • High-performance computing
  • Natural language processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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