title: Sparse classification for computer aided diagnosis using learned dictionaries.
abstract: 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, boosting, logistic regression, relevance vector machine (RVM), or kappa-nearest neighbor (KNN).
authors: University of Florida, Gainesville, FL 32611, USA. mliu@cise.ufl.edu
authors: Meizhu
authors: M
authors: Liu
authors: Le
authors: L
authors: Lu
authors: Xiaojing
authors: X
authors: Ye
authors: Shipeng
authors: S
authors: Yu
authors: Marcos
authors: M
authors: Salganicoff
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003111:Colonic Polyps / Q000175:diagnosis* / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007858:Learning; D015233:Models, Statistical; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011871:Radiology / Q000193:education* / Q000379:methods*; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging