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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bi, J., Xiong, T., Yu, S., Dundar, M., & Rao, R. B. (2008). An improved multi-task learning approach with applications in medical diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5211 LNAI, pp. 117–132). https://doi.org/10.1007/978-3-540-87479-9_26
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