Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammo graphic Risk Scoring
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Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammo graphic Risk Scoring
Abstract—Mammo graphic 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. < final year projects >
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