Rapidly Adaptive Cell Detection Using Transfer Learning with a Global Parameter

  • Nhat H. Nguyen
  • Eric Norris
  • Mark G. Clemens
  • Min C. Shin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


Recent advances in biomedical imaging have enabled the analysis of many different cell types. Learning-based cell detectors tend to be specific to a particular imaging protocol and cell type. For a new dataset, a tedious re-training process is required. In this paper, we present a novel method of training a cell detector on new datasets with minimal effort. First, we combine the classification rules extracted from existing data with the training samples of new data using transfer learning. Second, a global parameter is incorporated to refine the ranking of the classification rules. We demonstrate that our method achieves the same performance as previous approaches with only 10% of the training effort.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nhat H. Nguyen
    • 1
  • Eric Norris
    • 2
  • Mark G. Clemens
    • 2
  • Min C. Shin
    • 1
  1. 1.Department of Computer ScienceUniversity of North CarolinaCharlotte
  2. 2.Department of BiologyUniversity of North CarolinaCharlotte

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