Abstract: Deep Hashing for Large-Scale Medical Image Retrieval

  • Sailesh Conjeti
  • Magdalini Paschali
  • Abhijit Guha Roy
  • Nassir Navab
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Adoption of content-based image retrieval systems (CBIR) requires efficient indexing of the data contents in order to respond to visual queries without explicitly relying on textual keywords. Searching for similar data is closely related to the fundamental problem of nearest neighbor search. Exhaustive comparison of a query across the database is infeasible in large-scale retrieval as it is computationally expensive [1].

Literatur

  1. 1.
    Conjeti S, Katouzian A, Kazi A, et al. Metric hashing forests. Med Image Anal. 2016;34:13–29.Google Scholar
  2. 2.
    Conjeti S, Roy AG, Katouzian A, et al.; Springer. Hashing with residual networks for image retrieval. Proc MICCAI. 2017; p. 541–549.Google Scholar
  3. 3.
    Conjeti S, Paschali M, Katouzian A, et al. Deep multiple instance hashing for scalable medical image retrieval. Proc MICCAI. 2017; p. 550–558.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Sailesh Conjeti
    • 1
    • 2
  • Magdalini Paschali
    • 2
  • Abhijit Guha Roy
    • 2
    • 3
  • Nassir Navab
    • 2
    • 4
  1. 1.Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)BonnDeutschland
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenMünchenDeutschland
  3. 3.AI-medLudwig-Maximilian Universität MünchenMünchenDeutschland
  4. 4.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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