Deep hashing for large-scale medical image retrieval

Sailesh Conjeti; Magdalini Paschali; Abhijit Guha Roy; Nassir Navab

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

Deep hashing for large-scale medical image retrieval

Sailesh Conjeti, Magdalini Paschali, Abhijit Guha Roy, Nassir Navab

Informatics 16 - Chair of Computer Aided Medical Procedures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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].

Original language	English
Title of host publication	Informatik aktuell
Editors	Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus H. Maier-Hein, Christoph Palm, Thomas Tolxdorff
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	35
Number of pages	1
ISBN (Print)	9783540295945, 9783540748366, 9783540853237, 9783642246579, 9783642337062, 9783642413087, 9783662451083, 9783662557846, 9783662565360, 9783662580950
DOIs	https://doi.org/10.1007/978-3-662-56537-7_21
State	Published - 2018
Event	Workshop on Bildverarbeitung fur die Medizin, 2018 - Erlangen, Germany Duration: 11 Mar 2018 → 13 Mar 2018

Publication series

Name	Informatik aktuell
Volume	0
ISSN (Print)	1431-472X

Conference

Conference	Workshop on Bildverarbeitung fur die Medizin, 2018
Country/Territory	Germany
City	Erlangen
Period	11/03/18 → 13/03/18

Access to Document

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

Cite this

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title = "Deep hashing for large-scale medical image retrieval",

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].",

author = "Sailesh Conjeti and Magdalini Paschali and Roy, {Abhijit Guha} and Nassir Navab",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag GmbH Deutschland 2018.; Workshop on Bildverarbeitung fur die Medizin, 2018 ; Conference date: 11-03-2018 Through 13-03-2018",

year = "2018",

doi = "10.1007/978-3-662-56537-7_21",

language = "English",

isbn = "9783540295945",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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booktitle = "Informatik aktuell",

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Conjeti, S, Paschali, M, Roy, AG & Navab, N 2018, Deep hashing for large-scale medical image retrieval. in A Maier, TM Deserno, H Handels, KH Maier-Hein, C Palm & T Tolxdorff (eds), Informatik aktuell. Informatik aktuell, vol. 0, Springer Science and Business Media Deutschland GmbH, pp. 35, Workshop on Bildverarbeitung fur die Medizin, 2018, Erlangen, Germany, 11/03/18. https://doi.org/10.1007/978-3-662-56537-7_21

Deep hashing for large-scale medical image retrieval. / Conjeti, Sailesh; Paschali, Magdalini; Roy, Abhijit Guha et al.
Informatik aktuell. ed. / Andreas Maier; Thomas M. Deserno; Heinz Handels; Klaus H. Maier-Hein; Christoph Palm; Thomas Tolxdorff. Springer Science and Business Media Deutschland GmbH, 2018. p. 35 (Informatik aktuell; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Roy, Abhijit Guha

AU - Navab, Nassir

N1 - Publisher Copyright: © Springer-Verlag GmbH Deutschland 2018.

PY - 2018

Y1 - 2018

N2 - 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].

AB - 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].

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DO - 10.1007/978-3-662-56537-7_21

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A2 - Maier, Andreas

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A2 - Maier-Hein, Klaus H.

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PB - Springer Science and Business Media Deutschland GmbH

T2 - Workshop on Bildverarbeitung fur die Medizin, 2018

Y2 - 11 March 2018 through 13 March 2018

ER -

Deep hashing for large-scale medical image retrieval

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