Dense simple features for fast and accurate medical X-ray annotation

Uri Avni; Hayit Greenspan; Jacob Goldberger

doi:10.1007/978-3-642-15751-6_29

Dense simple features for fast and accurate medical X-ray annotation

Uri Avni, Hayit Greenspan, Jacob Goldberger

Signal Processing

Tel Aviv University

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

3 Scopus citations

Abstract

We present a simple, fast and accurate image categorization system, applied to medical image databases within the ImageCLEF 2009 medical annotation task. The methodology presented is based on local representation of the image content, using a bag of visual words approach in multiple scales, with a kernel based SVM classifier. The system was ranked first in this challenge, with total error score of 852.8.

Original language	English
Title of host publication	Multilingual Information Access Evaluation II
Subtitle of host publication	Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Revised Selected Papers
Publisher	Springer Verlag
Pages	239-246
Number of pages	8
ISBN (Print)	3642157505, 9783642157509
DOIs	https://doi.org/10.1007/978-3-642-15751-6_29
State	Published - 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6242 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Access to Document

10.1007/978-3-642-15751-6_29

Cite this

Avni, U., Greenspan, H., & Goldberger, J. (2010). Dense simple features for fast and accurate medical X-ray annotation. In Multilingual Information Access Evaluation II: Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Revised Selected Papers (pp. 239-246). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6242 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-15751-6_29

Avni, Uri ; Greenspan, Hayit ; Goldberger, Jacob. / Dense simple features for fast and accurate medical X-ray annotation. Multilingual Information Access Evaluation II: Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Revised Selected Papers. Springer Verlag, 2010. pp. 239-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9e817e88ed274150b5891b55819aa11e,

title = "Dense simple features for fast and accurate medical X-ray annotation",

abstract = "We present a simple, fast and accurate image categorization system, applied to medical image databases within the ImageCLEF 2009 medical annotation task. The methodology presented is based on local representation of the image content, using a bag of visual words approach in multiple scales, with a kernel based SVM classifier. The system was ranked first in this challenge, with total error score of 852.8.",

author = "Uri Avni and Hayit Greenspan and Jacob Goldberger",

year = "2010",

doi = "10.1007/978-3-642-15751-6_29",

language = "אנגלית",

isbn = "3642157505",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "239--246",

booktitle = "Multilingual Information Access Evaluation II",

address = "גרמניה",

}

Avni, U, Greenspan, H & Goldberger, J 2010, Dense simple features for fast and accurate medical X-ray annotation. in Multilingual Information Access Evaluation II: Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6242 LNCS, Springer Verlag, pp. 239-246. https://doi.org/10.1007/978-3-642-15751-6_29

Dense simple features for fast and accurate medical X-ray annotation. / Avni, Uri; Greenspan, Hayit; Goldberger, Jacob.
Multilingual Information Access Evaluation II: Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Revised Selected Papers. Springer Verlag, 2010. p. 239-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6242 LNCS).

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

TY - GEN

T1 - Dense simple features for fast and accurate medical X-ray annotation

AU - Avni, Uri

AU - Greenspan, Hayit

AU - Goldberger, Jacob

PY - 2010

Y1 - 2010

N2 - We present a simple, fast and accurate image categorization system, applied to medical image databases within the ImageCLEF 2009 medical annotation task. The methodology presented is based on local representation of the image content, using a bag of visual words approach in multiple scales, with a kernel based SVM classifier. The system was ranked first in this challenge, with total error score of 852.8.

AB - We present a simple, fast and accurate image categorization system, applied to medical image databases within the ImageCLEF 2009 medical annotation task. The methodology presented is based on local representation of the image content, using a bag of visual words approach in multiple scales, with a kernel based SVM classifier. The system was ranked first in this challenge, with total error score of 852.8.

UR - http://www.scopus.com/inward/record.url?scp=78049336945&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15751-6_29

DO - 10.1007/978-3-642-15751-6_29

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:78049336945

SN - 3642157505

SN - 9783642157509

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 239

EP - 246

BT - Multilingual Information Access Evaluation II

PB - Springer Verlag

ER -

Avni U, Greenspan H, Goldberger J. Dense simple features for fast and accurate medical X-ray annotation. In Multilingual Information Access Evaluation II: Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Revised Selected Papers. Springer Verlag. 2010. p. 239-246. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15751-6_29

Dense simple features for fast and accurate medical X-ray annotation

Abstract

Publication series

Access to Document

Other files and links

Fingerprint

Cite this