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A Unified Mammogram Analysis Method via Hybrid Deep Supervision

  • Rongzhao ZhangEmail author
  • Han Zhang
  • Albert C. S. Chung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system. In this work, we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation. Our model is designed based on a deep U-Net with residual connections, and equipped with the novel hybrid deep supervision (HDS) scheme for end-to-end multi-task learning. As an extension of deep supervision (DS), HDS not only can force the model to learn more discriminative features like DS, but also seamlessly integrates segmentation and classification tasks into one model, thus the model can benefit from both pixel-wise and image-wise supervisions. We extensively validate the proposed method on the widely-used INbreast dataset. Ablation study corroborates that pixel-wise and image-wise supervisions are mutually beneficial, evidencing the efficacy of HDS. The results of 5-fold cross validation indicate that our unified model matches state-of-the-art performance on both mammogram segmentation and classification tasks, which achieves an average segmentation Dice similarity coefficient (DSC) of 0.85 and a classification accuracy of 0.89. The code is available at https://github.com/angrypudding/hybrid-ds.

Keywords

Whole mammogram classification Mass segmentation Deep supervision 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rongzhao Zhang
    • 1
    Email author
  • Han Zhang
    • 1
  • Albert C. S. Chung
    • 1
  1. 1.The Hong Kong University of Science and TechnologyKowloonHong Kong

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