title: Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.
abstract: 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.
authors.forename: Michiel
authors.initials: M
authors.lastname: Kallenberg
authors.forename: Kersten
authors.initials: K
authors.lastname: Petersen
authors.forename: Mads
authors.initials: M
authors.lastname: Nielsen
authors.forename: Andrew Y
authors.initials: AY
authors.lastname: Ng
authors.lastname: Pengfei Diao
authors.forename: Christian
authors.initials: C
authors.lastname: Igel
authors.forename: Celine M
authors.initials: CM
authors.lastname: Vachon
authors.forename: Katharina
authors.initials: K
authors.lastname: Holland
authors.forename: Rikke Rass
authors.initials: RR
authors.lastname: Winkel
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
authors.forename: Martin
authors.initials: M
authors.lastname: Lillholm
mesh_terms: D000328:Adult; D000368:Aged; D001940:Breast / Q000000981:diagnostic imaging*; D000071060:Breast Density / Q000502:physiology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D012307:Risk Factors; D000069558:Unsupervised Machine Learning