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

Part of the Lecture Notes in Computer Science book series (LNIP,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

Y. Liu and Z. Zhou—Equal contribution.

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Acknowledgement

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

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Correspondence to Yizhou Yu .

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Liu, Y. et al. (2019). From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_53

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  • DOI: https://doi.org/10.1007/978-3-030-32226-7_53

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