Breast cancer detection/diagnosis with upstream data fusion and machine learning

David W. Porter, William C. Walton, Susan C. Harvey, Lisa A. Mullen, Benjamin M.W. Tsui, Seung Jun Kim, Keith S. Peyton

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

Abstract

Machine learning (ML) has made great advancements in imaging for breast cancer detection, including reducing radiologists read times, yet its performance is still reported to be at best similar to that of expert radiologists. This leaves a performance gap between what is desired by radiologists and what can actually be achieved in terms of early detection, reduction of excessive false positives and minimization of unnecessary biopsies. We have seen a similar situation with military intelligence that is expressed by operators as "drowning in data and starving for information". We invented Upstream Data Fusion (UDF) to help fill the gap. ML is used to produce candidate detections for individual sensing modalities with high detection rates and high false positive rates. Data fusion is used to combine modalities and dramatically diminish false positives. Upstream data, that is closer to raw data, is hard for operators to visualize. Yet it is used for fusion to recover information that would otherwise be lost by the processing to make it visually acceptable to humans. Our research with breast cancer detection involving the fusion of Digital Breast Tomosynthesis (DBT) with Magnetic Resonance Imaging (MRI) and also the fusion of DBT with ultrasound (US) data has yielded preliminary results which lead us to conclude that UDF can help to both fill the performance gap and reduce radiologist read time. Our findings suggest that UDF, combined with ML techniques, can result in paradigm changes in the achievable accuracy and efficiency of early breast cancer detection.

Original languageEnglish (US)
Title of host publication15th International Workshop on Breast Imaging, IWBI 2020
EditorsHilde Bosmans, Nicholas Marshall, Chantal Van Ongeval
PublisherSPIE
ISBN (Electronic)9781510638310
DOIs
StatePublished - Jan 1 2020
Event15th International Workshop on Breast Imaging, IWBI 2020 - Leuven, Belgium
Duration: May 25 2020May 27 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11513
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Workshop on Breast Imaging, IWBI 2020
CountryBelgium
CityLeuven
Period5/25/205/27/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Porter, D. W., Walton, W. C., Harvey, S. C., Mullen, L. A., Tsui, B. M. W., Kim, S. J., & Peyton, K. S. (2020). Breast cancer detection/diagnosis with upstream data fusion and machine learning. In H. Bosmans, N. Marshall, & C. Van Ongeval (Eds.), 15th International Workshop on Breast Imaging, IWBI 2020 [115130X] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11513). SPIE. https://doi.org/10.1117/12.2564159