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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 45–54Cite as

Stacked Autoencoders for Medical Image Search

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Abstract

Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate image search. This work presents a feature extraction technique for medical images using stacked autoencoders, which encode images to binary vectors. The technique is applied to the IRMA dataset, a collection of 14,410 x-ray images in order to demonstrate the ability of autoencoders to retrieve similar x-rays given test queries. Using IRMA dataset as a benchmark, it was found that stacked autoencoders gave excellent results with a retrieval error of 376 for 1,733 test images with a compression of 74.61%.

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Author information

Authors and Affiliations

  1. Systems Design Engineering, University of Waterloo, Waterloo, Canada

    S. Sharma, I. Umar, L. Ospina & D. Wong

  2. KIMIA Lab, University of Waterloo, Waterloo, Canada

    H. R. Tizhoosh

Authors
  1. S. Sharma
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  2. I. Umar
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  3. L. Ospina
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  4. D. Wong
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  5. H. R. Tizhoosh
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Corresponding author

Correspondence to H. R. Tizhoosh .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Cite this paper

Sharma, S., Umar, I., Ospina, L., Wong, D., Tizhoosh, H.R. (2016). Stacked Autoencoders for Medical Image Search. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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