An Improved Multi-task Learning Approach with Applications in Medical Diagnosis

  • Jinbo Bi
  • Tao Xiong
  • Shipeng Yu
  • Murat Dundar
  • R. Bharat Rao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5211)


We propose a family of multi-task learning algorithms for collaborative computer aided diagnosis which aims to diagnose multiple clinically-related abnormal structures from medical images. Our formulations eliminate features irrelevant to all tasks, and identify discriminative features for each of the tasks. A probabilistic model is derived to justify the proposed learning formulations. By equivalence proof, some existing regularization-based methods can also be interpreted by our probabilistic model as imposing a Wishart hyperprior. Convergence analysis highlights the conditions under which the formulations achieve convexity and global convergence. Two real-world medical problems: lung cancer prognosis and heart wall motion analysis, are used to validate the proposed algorithms.


Loss Function Ground Glass Opacity Multitask Learning Training Sample Size Convex Loss Function 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jinbo Bi
    • 1
  • Tao Xiong
    • 2
  • Shipeng Yu
    • 1
  • Murat Dundar
    • 1
  • R. Bharat Rao
    • 1
  1. 1.CAD and Knowledge Solutions, Siemens Medical SolutionsMalvernUSA
  2. 2.Risk Management, Applied Research, eBay Inc.San JoseUSA

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