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.
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This product was added to our catalog on Tuesday 30 May, 2017.