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Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection

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Machine Learning in Medical Imaging (MLMI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10019))

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Abstract

Approaches for detecting regions of interest in biomedical image data mostly assume that a large amount of annotated training data is available. Certainly, for unchanging problem definitions, the acquisition of large annotated data is time consuming, yet feasible. However, distinct practical problems with large training corpi arise if variability due to different imaging conditions or inter-personal variations lead to significant changes in the image representation. To circumvent these issues, we investigate a classifier learning scenario which requires a small amount of positive annotation data only. Contrarily to previous approaches which focus on methodologies to explicitly or implicitly deal with specific classification scenarios (such as one-class classification), we show that existing supervised classification models can handle a changed setting effectively without any specific modifications.

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Notes

  1. 1.

    We use the MATLAB C-SVM implementation.

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Acknowledgments

This work was supported by the German Research Foundation (DFG), grant number ME3737/3-1.

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Authors and Affiliations

  1. Aachen Center for Biomedical Image Analysis, Visualization and Exploration (ACTIVE), Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

    Michael Gadermayr & Dorit Merhof

  2. Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany

    Barbara Mara Klinkhammer & Peter Boor

Authors
  1. Michael Gadermayr
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  2. Barbara Mara Klinkhammer
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  3. Peter Boor
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  4. Dorit Merhof
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Corresponding author

Correspondence to Michael Gadermayr .

Editor information

Editors and Affiliations

  1. University of North Carolina , Chapel Hill, North Carolina, USA

    Li Wang

  2. University of North Carolina , Chapel Hill, North Carolina, USA

    Ehsan Adeli

  3. Shanghai Jiaotong University , Shanghai, China

    Qian Wang

  4. Nanjing University , Nanjing, China

    Yinghuan Shi

  5. Korea University , Seoul, Korea (Republic of)

    Heung-Il Suk

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Gadermayr, M., Klinkhammer, B.M., Boor, P., Merhof, D. (2016). Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-47157-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47156-3

  • Online ISBN: 978-3-319-47157-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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