Adversarial deep structured nets for mass segmentation from mammograms

Wentao Zhu, Xiang Xiang, Trac D. Tran, Gregory D. Hager, Xiaohui Xie

Research output: Contribution to journalArticlepeer-review


Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a conditional random field (CRF) to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Oct 24 2017


  • Adversarial deep structured networks
  • Adversarial fully convolutional networks
  • Segmentation

ASJC Scopus subject areas

  • General

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