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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 157–164Cite as

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A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slides

A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slides

  • Angel Cruz-Roa19,
  • Fabio González19,
  • Joseph Galaro20,
  • Alexander R. Judkins21,
  • David Ellison22,
  • Jennifer Baccon23,
  • Anant Madabhushi20 &
  • …
  • Eduardo Romero19 
  • Conference paper
  • 5549 Accesses

  • 8 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average.

Keywords

  • Visual Word
  • Local Descriptor
  • Latent Semantic Analysis
  • Probabilistic Latent Semantic Analysis
  • Histopathological Image

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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Author information

Authors and Affiliations

  1. BioIngenium Research Group, Universidad Nacional de Colombia, Bogotá, Colombia

    Angel Cruz-Roa, Fabio González & Eduardo Romero

  2. Department of Biomedical Engineering, Rutgers, Piscataway, NJ, USA

    Joseph Galaro & Anant Madabhushi

  3. Department of Pathology Lab Medicine, Children Hospital of L.A., Los Angeles, CA, USA

    Alexander R. Judkins

  4. St. Jude Children’s Research Hospital from Memphis, TN, USA

    David Ellison

  5. Department of Pathology, Penn State College of Medicine, Hershey, PA, USA

    Jennifer Baccon

Authors
  1. Angel Cruz-Roa
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  2. Fabio González
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  3. Joseph Galaro
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  4. Alexander R. Judkins
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  5. David Ellison
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  6. Jennifer Baccon
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  7. Anant Madabhushi
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  8. Eduardo Romero
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Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Cruz-Roa, A. et al. (2012). A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slides. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_20

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  • DOI: https://doi.org/10.1007/978-3-642-33415-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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