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

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

 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.