05978907

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05978907 Liu, Meizhu Lu, Le Ye, Xiaojing Yu, Shipeng Salganicoff, Marcos Sparse classification for computer aided diagnosis using learned dictionaries. Fichtinger, Gabor (ed.) et al., Medical image computing and computer-assisted intervention -- MICCAI 2011. 14th international conference, Toronto, Canada, September 18--22, 2011. Proceedings, Part III. Berlin: Springer (ISBN 978-3-642-23625-9/pbk). Lecture Notes in Computer Science 6893, 41-48 (2011). 2011 Berlin: Springer EN

doi:10.1007/978-3-642-23626-6_6

Summary: Classification is one of the core problems in computer-aided cancer diagnosis (CAD) via medical image interpretation. High detection sensitivity with reasonably low false positive (FP) rate is essential for any CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. In this paper, we propose a novel classification framework based on sparse representation. It first builds an overcomplete dictionary of atoms for each class via $K$-SVD learning, then classification is formulated as sparse coding which can be solved efficiently. This representation naturally generalizes for both binary and multiwise classification problems, and can be used as a standalone classifier or integrated with an existing decision system. Our method is extensively validated in CAD systems for both colorectal polyp and lung nodule detection, using hospital scale, multi-site clinical datasets. The results show that we achieve superior classification performance than existing state-of-the-arts, using support vector machine (SVM) and its variants [1,2], boosting [3], logistic regression [4], relevance vector machine (RVM) [5,6], or $k$-nearest neighbor (KNN) [7].