2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification

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

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

15 Scopus citations

Abstract

Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Pages1013-1017
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

Funding Information:
This study is sponsored by Grant No. IRG 16-182-28 from the American Cancer Society and Grant No. IIS-1553116 from the National Science Foundation.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Breast Cancer
  • Convolutional Neural Networks
  • Full-Field Digital Mammography
  • Full-Volume 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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