A Generative Statistical Model of Mammographic Appearance

(The paper appears in its original form on the next page.) @inproceedings{CJR-MIUA-2004, author = {C. abstract = {We present a generative parametric statistical model of the appearance of entire digital x-ray mammograms. Computer-aided detection (CADe) in mammography has traditionally been treated as a pattern recognition task, where an attempt is made to emulate radiologists' interpretation strategies. We propose instead that CADe be performed via novelty (outlier) detection [1]. Not only are there far more pathology-free than abnormal mammograms from which to learn, but novelty detection would detect all abnormal features, rather than specific classes. This requires a model of normal mammographic appearance that allows novel (unlikely) model instances to be identified , suggesting that a statistical model is appropriate. Our model addresses many of the problems associated with modelling the appearance of entire mammograms for novelty detection, but this paper does not focus on using the model in the novelty detection scenario. We propose a method for novelty detection, and offer a discussion of the work, in section 5.} }