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Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries

  • Meizhu Liu
  • Le Lu
  • Xiaojing Ye
  • Shipeng Yu
  • Marcos Salganicoff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

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 [1,2], boosting [3], logistic regression [4], relevance vector machine (RVM) [5,6], or k-nearest neighbor (KNN) [7].

Keywords

Singular Value Decomposition Sparse Representation Sparse Code Learn Dictionary Orthogonal Match Pursuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Meizhu Liu
    • 1
    • 2
  • Le Lu
    • 1
    • 2
  • Xiaojing Ye
    • 1
    • 2
  • Shipeng Yu
    • 1
    • 2
  • Marcos Salganicoff
    • 1
    • 2
  1. 1.Siemens Medical SolutionsMalvernUSA
  2. 2.University of FloridaGainesvilleUSA

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