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From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network

  • Yuhang Liu
  • Zhen Zhou
  • Shu Zhang
  • Ling Luo
  • Qianyi Zhang
  • Fandong Zhang
  • Xiuli Li
  • Yizhou Wang
  • Yizhou YuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

The comparison of bilateral mammogram images is important for finding masses especially in dense breasts. However, most existing mammogram mass detection algorithms only considered unilateral image. In this paper, we propose a deep model called contrasted bilateral network (CBN) to take bilateral information into consideration. In CBN, Mask R-CNN is used as a basic framework, upon which two major modules are developed to exploit the bilateral information: distortion insensitive comparison module and logic guided bilateral module. The former one is designed to be robust to nonrigid distortion of bilateral registration, while the latter one integrates the bilateral domain knowledge of radiologist. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves the state-of-the-art performance.

Keywords

Mammogram mass Object detection Domain knowledge 

Notes

Acknowledgement

This work is funded by the National Natural Science Foundation of China (Grant No. 61625201, 61527804).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuhang Liu
    • 1
  • Zhen Zhou
    • 2
  • Shu Zhang
    • 2
  • Ling Luo
    • 1
    • 3
  • Qianyi Zhang
    • 1
  • Fandong Zhang
    • 4
  • Xiuli Li
    • 1
  • Yizhou Wang
    • 1
    • 2
    • 5
  • Yizhou Yu
    • 1
    Email author
  1. 1.Deepwise AI LabBeijingChina
  2. 2.Computer Science DepartmentPeking UniversityBeijingChina
  3. 3.Beijing University of Posts and TelecommunicationsBeijingChina
  4. 4.Center for Data SciencePeking UniversityBeijingChina
  5. 5.Peng Cheng LaboratoryShenzhenChina

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