Joint 2D-3D Breast Cancer Classification

Gongbo Liang, Xiaoqin Wang, Yu Zhang, Xin Xing, Hunter Blanton, Tawfiq Salem, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Scopus citations

Abstract

Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Pages692-696
Number of pages5
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: Nov 18 2019Nov 21 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/TerritoryUnited States
CitySan Diego
Period11/18/1911/21/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Digital mammography
  • clinical inspired
  • convolutional neural network
  • digital breast tomosynthesis

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

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