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Scalable Histopathological Image Analysis via Active Learning

  • Yan Zhu
  • Shaoting Zhang
  • Wei Liu
  • Dimitris N. Metaxas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Training an effective and scalable system for medical image analysis usually requires a large amount of labeled data, which incurs a tremendous annotation burden for pathologists. Recent progress in active learning can alleviate this issue, leading to a great reduction on the labeling cost without sacrificing the predicting accuracy too much. However, most existing active learning methods disregard the “structured information” that may exist in medical images (e.g., data from individual patients), and make a simplifying assumption that unlabeled data is independently and identically distributed. Both may not be suitable for real-world medical images. In this paper, we propose a novel batch-mode active learning method which explores and leverages such structured information in annotations of medical images to enforce diversity among the selected data, therefore maximizing the information gain. We formulate the active learning problem as an adaptive submodular function maximization problem subject to a partition matroid constraint, and further present an efficient greedy algorithm to achieve a good solution with a theoretically proven bound. We demonstrate the efficacy of our algorithm on thousands of histopathological images of breast microscopic tissues.

Keywords

Active Learning Submodular Function Histopathological Image Active Learning Method Label Cost 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Yan Zhu
    • 1
  • Shaoting Zhang
    • 2
  • Wei Liu
    • 3
  • Dimitris N. Metaxas
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  2. 2.Department of Computer ScienceUniversity of North Carolina at CharlotteUSA
  3. 3.IBM T.J. Watson Research CenterUSA

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