Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

Michiel Kallenberg, Kersten Petersen, Mads Nielsen, Andrew Y. Ng, Pengfei Diao, Christian Igel, Celine M. Vachon, Katharina Holland, Rikke Rass Winkel, Nico Karssemeijer, Martin Lillholm

Research output: Contribution to journalArticlepeer-review

233 Scopus citations


Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Original languageEnglish (US)
Article number7412749
Pages (from-to)1322-1331
Number of pages10
JournalIEEE transactions on medical imaging
Issue number5
StatePublished - May 2016


  • Breast cancer
  • deep learning
  • mammograms
  • prognosis
  • risk factor
  • segmentation
  • unsupervised feature learning

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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