document: Computer aided detection via asymmetric cascade of sparse hyperplane classifiers This paper describes a novel classification method for computer aided detection ( CAD ) that identifies structures of interest from medical images . CAD problems are challenging largely due to the following three characteristics . Typical CAD training data sets are large and extremely unbalanced between positive and negative classes . When searching for descriptive features , researchers often deploy a large set of experimental features , which consequently introduces irrelevant and redundant features . Finally , a CAD system has to satisfy stringent real-time requirements . This work is distinguished by three key contributions . The first is a cascade classification approach which is able to tackle all the above difficulties in a unified framework by employing an asymmetric cascade of sparse classifiers each trained to achieve high detection sensitivity and satisfactory false positive rates . The second is the incorporation of feature computational costs in a linear program formulation that allows the feature selection process to take into account different evaluation costs of various features . The third is a boosting algorithm derived from column generation optimization to effectively solve the proposed cascade linear programs . We apply the proposed approach to the problem of detecting lung nodules from helical multi-slice CT images . Our approach demonstrates superior performance in comparison against support vector machines , linear discriminant analysis and cascade AdaBoost . Especially , the resulting detection system is significantly sped up with our approach .
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