HKBU at mediaeval 2017 medico: Medical multimedia task

Yang Liu; Zhonglei Gu; William Kwok Wai Cheung

HKBU at mediaeval 2017 medico: Medical multimedia task

Yang Liu, Zhonglei Gu, William Kwok Wai Cheung

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

28 Downloads (Pure)

Abstract

In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.

Original language	English
Title of host publication	Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017)
Editors	Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler , Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J. F. Jones, Martha Larson
Publisher	CEUR-WS
Number of pages	3
Publication status	Published - Sept 2017
Event	2017 Multimedia Benchmark Workshop, MediaEval 2017 - Dublin, Ireland Duration: 13 Sept 2017 → 15 Sept 2017 https://ceur-ws.org/Vol-1984/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
Volume	1984
ISSN (Print)	1613-0073

Conference

Conference	2017 Multimedia Benchmark Workshop, MediaEval 2017
Country/Territory	Ireland
City	Dublin
Period	13/09/17 → 15/09/17
Internet address	https://ceur-ws.org/Vol-1984/

Scopus Subject Areas

General Computer Science

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https://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_7.pdf

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Liu, Y., Gu, Z., & Cheung, W. K. W. (2017). HKBU at mediaeval 2017 medico: Medical multimedia task. In G. Gravier, B. Bischke, C.-H. Demarty, M. Zaharieva, M. Riegler , E. Dellandrea, D. Bogdanov, R. Sutcliffe, G. J. F. Jones, & M. Larson (Eds.), Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017) (CEUR Workshop Proceedings; Vol. 1984). CEUR-WS. https://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_7.pdf

Liu, Yang ; Gu, Zhonglei ; Cheung, William Kwok Wai. / HKBU at mediaeval 2017 medico : Medical multimedia task. Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). editor / Guillaume Gravier ; Benjamin Bischke ; Claire-Hélène Demarty ; Maia Zaharieva ; Michael Riegler ; Emmanuel Dellandrea ; Dmitry Bogdanov ; Richard Sutcliffe ; Gareth J. F. Jones ; Martha Larson. CEUR-WS, 2017. (CEUR Workshop Proceedings).

@inproceedings{ae5bd6866560446c88b66166a08e744c,

title = "HKBU at mediaeval 2017 medico: Medical multimedia task",

abstract = "In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.",

author = "Yang Liu and Zhonglei Gu and Cheung, {William Kwok Wai}",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.; 2017 Multimedia Benchmark Workshop, MediaEval 2017 ; Conference date: 13-09-2017 Through 15-09-2017",

year = "2017",

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language = "English",

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editor = "Guillaume Gravier and Benjamin Bischke and Claire-H{\'e}l{\`e}ne Demarty and Maia Zaharieva and {Riegler }, Michael and Emmanuel Dellandrea and Dmitry Bogdanov and Richard Sutcliffe and Jones, {Gareth J. F.} and Martha Larson",

booktitle = "Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017)",

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Liu, Y, Gu, Z & Cheung, WKW 2017, HKBU at mediaeval 2017 medico: Medical multimedia task. in G Gravier, B Bischke, C-H Demarty, M Zaharieva, M Riegler , E Dellandrea, D Bogdanov, R Sutcliffe, GJF Jones & M Larson (eds), Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). CEUR Workshop Proceedings, vol. 1984, CEUR-WS, 2017 Multimedia Benchmark Workshop, MediaEval 2017, Dublin, Ireland, 13/09/17. <https://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_7.pdf>

HKBU at mediaeval 2017 medico: Medical multimedia task. / Liu, Yang; Gu, Zhonglei; Cheung, William Kwok Wai.
Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). ed. / Guillaume Gravier; Benjamin Bischke; Claire-Hélène Demarty; Maia Zaharieva; Michael Riegler ; Emmanuel Dellandrea; Dmitry Bogdanov; Richard Sutcliffe; Gareth J. F. Jones; Martha Larson. CEUR-WS, 2017. (CEUR Workshop Proceedings; Vol. 1984).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.

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Liu Y, Gu Z, Cheung WKW. HKBU at mediaeval 2017 medico: Medical multimedia task. In Gravier G, Bischke B, Demarty CH, Zaharieva M, Riegler M, Dellandrea E, Bogdanov D, Sutcliffe R, Jones GJF, Larson M, editors, Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). CEUR-WS. 2017. (CEUR Workshop Proceedings).

HKBU at mediaeval 2017 medico: Medical multimedia task

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