@inproceedings{28e25c752d8c4e9d96ce81fb83d42202,
title = "Deep learning analytics for diagnostic support of breast cancer disease management",
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.",
author = "Tiancheng He and Mamta Puppala and Richard Ogunti and Mancuso, {James J.} and Xiaohui Yu and Shenyi Chen and Chang, {Jenny C.} and Patel, {Tejal A.} and Wong, {Stephen T.C.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 ; Conference date: 16-02-2017 Through 19-02-2017",
year = "2017",
month = apr,
day = "11",
doi = "10.1109/BHI.2017.7897281",
language = "English (US)",
series = "2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "365--368",
booktitle = "2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017",
address = "United States",
}