Breast cancer classification using deep transfer learning on structured healthcare data

Akram Farhadi, David Chen, Rozalina McCoy, Christopher Scott, John A. Miller, Celine M. Vachon, Che Ngufor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Efforts to improve early identification of aggressive high grade breast cancers, which pose the greatest risk to patient health if not detected early, are hindered by the rarity of these events. To address this problem, we proposed an accurate and efficient deep transfer learning method to handle the imbalanced data problem that is prominent in breast cancer data. In contrast to existing approaches based primarily on large image databases, we focused on structured data, which has not been commonly used for deep transfer learning. We used a number of publicly available breast cancer data sets to generate a 'pre-trained' model and transfer learned concepts to predict high grade malignant tumors in patients diagnosed with breast cancer at Mayo Clinic. We compared our results with state-of-the-art techniques for addressing the problem of imbalanced learning and confirmed the superiority of the proposed method. To further demonstrate the ability of the proposed method to handle different degrees of class imbalance, a series of experiments were performed on publicly available breast cancer data under simulated class imbalanced settings. Based on the experimental results, we concluded that the proposed deep transfer learning on structured data can be used as an efficient method to handle imbalanced class problems in clinical research.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
EditorsLisa Singh, Richard De Veaux, George Karypis, Francesco Bonchi, Jennifer Hill
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages277-286
Number of pages10
ISBN (Electronic)9781728144931
DOIs
StatePublished - Oct 2019
Event6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 - Washington, United States
Duration: Oct 5 2019Oct 8 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019

Conference

Conference6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
CountryUnited States
CityWashington
Period10/5/1910/8/19

Keywords

  • Breast cancer
  • Class imbalance
  • Deep learning
  • Deep transfer learning
  • SMOTE

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Farhadi, A., Chen, D., McCoy, R., Scott, C., Miller, J. A., Vachon, C. M., & Ngufor, C. (2019). Breast cancer classification using deep transfer learning on structured healthcare data. In L. Singh, R. De Veaux, G. Karypis, F. Bonchi, & J. Hill (Eds.), Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 (pp. 277-286). [8964176] (Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2019.00043