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Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features

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Machine Learning in Medical Imaging (MLMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

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Abstract

In this paper we propose a method able to automatically detect good/bad colonies of iPS cells using local patches based on densely extracted SIFT features. Different options for local patch classification based on a kernelized novelty detector, a 2-class SVM and a local Bag-of-Features approach are considered. Experimental results on 33 images of iPS cell colonies have shown that excellent accuracy can be achieved by the proposed approach.

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Correspondence to Bisser Raytchev .

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© 2015 Springer International Publishing Switzerland

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Masuda, A. et al. (2015). Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-24888-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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