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 t...

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Veröffentlicht in:	IEEE transactions on medical imaging 2016-05, Vol.35 (5), p.1322-1331
Hauptverfasser:	Kallenberg, Michiel, Petersen, Kersten, Nielsen, Mads, Ng, Andrew Y., Pengfei Diao, Igel, Christian, Vachon, Celine M., Holland, Katharina, Winkel, Rikke Rass, Karssemeijer, Nico, Lillholm, Martin
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Breast Breast - diagnostic imaging Breast cancer Breast Density - physiology Breast Neoplasms - diagnostic imaging Breast Neoplasms - epidemiology Breasts Computer architecture deep learning Density Female Humans Image Interpretation, Computer-Assisted - methods Image segmentation Machine learning mammograms Mammography Mammography - methods Manuals Mathematical models Middle Aged prognosis Risk risk factor Risk Factors Scoring Segmentation Sparsity Surface layer Texture unsupervised feature learning Unsupervised Machine Learning
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