Deep learning analytics for diagnostic support of breast cancer disease management

Tiancheng He, Mamta Puppala, Richard Ogunti, James J. Mancuso, Xiaohui Yu, Shenyi Chen, Jenny C. Chang, Tejal A. Patel, Stephen T.C. Wong

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

10 Scopus citations

Abstract

Breast cancer continues to be one of the leading causes of cancer death among women. Mammogram is the standard of care for screening and diagnosis of breast cancer. The American College of Radiology developed the Breast Imaging Reporting and Data System (BI-RADS) lexicon to standardize mammographic reporting to assess cancer risk and facilitate biopsy decision-making. However, because substantial inter-observer variability remains in the application of the BI-RADS lexicon, including inappropriate term usage and missing data, current biopsy decision-making accuracy using the unstructured free text or semi-structured reports varies greatly. Hence, incorporating novel and accurate technique into breast cancer decision-making data is critical. Here, we combined natural language processing and deep learning methods to develop an analytic model that targets well-characterized and defined specific breast suspicious patient subgroups rather than a broad heterogeneous group for diagnostic support of breast cancer management.

Original languageEnglish (US)
Title of host publication2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages365-368
Number of pages4
ISBN (Electronic)9781509041794
DOIs
StatePublished - Apr 11 2017
Event4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 - Orlando, United States
Duration: Feb 16 2017Feb 19 2017

Publication series

Name2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

Conference

Conference4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
CountryUnited States
CityOrlando
Period2/16/172/19/17

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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