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

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Abstract

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.

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Author information

Authors and Affiliations

  1. CAD and Knowledge Solutions, Siemens Medical Solutions, 20 Valley Stream Parkway, Malvern, PA 19355, USA

    Jinbo Bi, Shipeng Yu, Murat Dundar & R. Bharat Rao

  2. Risk Management, Applied Research, eBay Inc., 2145 Hamilton Avenue, San Jose, CA 95125, USA

    Tao Xiong

Authors
  1. Jinbo Bi

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  2. Tao Xiong

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  3. Shipeng Yu

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  4. Murat Dundar

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  5. R. Bharat Rao

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Editor information

Walter Daelemans Bart Goethals Katharina Morik

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© 2008 Springer-Verlag Berlin Heidelberg

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Bi, J., Xiong, T., Yu, S., Dundar, M., Rao, R.B. (2008). An Improved Multi-task Learning Approach with Applications in Medical Diagnosis. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87479-9_26

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