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Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

  • Wentao ZhuEmail author
  • Qi Lou
  • Yeeleng Scott Vang
  • Xiaohui Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations. (Code: https://github.com/wentaozhu/deep-mil-for-whole-mammogram-classification.git).

Keywords

Deep multi-instance learning Whole mammogram classification Max pooling-based MIL Label assignment-based MIL Sparse MIL 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wentao Zhu
    • 1
    Email author
  • Qi Lou
    • 1
  • Yeeleng Scott Vang
    • 1
  • Xiaohui Xie
    • 1
  1. 1.Department of Computer ScienceUniversity of California, IrvineIrvineUSA

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