Deep hashing for large-scale medical image retrieval

Conjeti S, Paschali M, Roy AG, Navab N (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 0

Pages Range: 35-

Conference Proceedings Title: Informatik aktuell

Event location: Erlangen, DEU

ISBN: 9783540295945

DOI: 10.1007/978-3-662-56537-7_21

Abstract

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].

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) DE Germany (DE)

How to cite

APA:

Conjeti, S., Paschali, M., Roy, A.G., & Navab, N. (2018). Deep hashing for large-scale medical image retrieval. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus H. Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 35-). Erlangen, DEU: Springer Science and Business Media Deutschland GmbH.

MLA:

Conjeti, Sailesh, et al. "Deep hashing for large-scale medical image retrieval." Proceedings of the Workshop on Bildverarbeitung fur die Medizin, 2018, Erlangen, DEU Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus H. Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2018. 35-.

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