title: A ℓ2, 1norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.
abstract: OBJECTIVE
  The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD).
  
  
  METHODS
  In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ2, 1norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ2, 1regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ2, 1norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method.
  
  
  RESULTS
  The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ2, 1norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods.
  
  
  CONCLUSIONS
  The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ2, 1norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature.
authors.affiliation: Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China. Electronic address: caopeng@cse.neu.edu.cn.
authors.forename: Peng
authors.initials: P
authors.lastname: Cao
authors.affiliation: Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.affiliation: Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
authors.forename: Dazhe
authors.initials: D
authors.lastname: Zhao
authors.affiliation: Information Science and Engineering, Northeastern University, Shenyang, China.
authors.forename: Min
authors.initials: M
authors.lastname: Huang
authors.affiliation: Computing Science, University of Alberta, Edmonton, Alberta, Canada.
authors.forename: Osmar
authors.initials: O
authors.lastname: Zaiane
mesh_terms: D003936:Diagnosis, Computer-Assisted; D005189:False Positive Reactions; D006801:Humans; D000069550:Machine Learning; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis*; D060388:Support Vector Machine
keywords: Classification; False positive reduction; Heterogeneous feature fusion; Lung nodule detection; Multi-kernel learning