Abstract
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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© 2011 Springer-Verlag Berlin Heidelberg
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Nguyen, N.H., Norris, E., Clemens, M.G., Shin, M.C. (2011). Rapidly Adaptive Cell Detection Using Transfer Learning with a Global Parameter. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_26
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DOI: https://doi.org/10.1007/978-3-642-24319-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24318-9
Online ISBN: 978-3-642-24319-6
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