Data Fusion for multi-lesion diabetic retinopathy detection

Herbert Jelinek; R. Pires; R. Padilha; S. Goldenstein; J. Wainer; Terence Bossomaier; A. Rocha

doi:10.1109/CBMS.2012.6266342

Data Fusion for multi-lesion diabetic retinopathy detection

Herbert Jelinek, R. Pires, R. Padilha, S. Goldenstein, J. Wainer, Terence Bossomaier, A. Rocha

Research output: Book chapter/Published conference paper › Conference paper › peer-review

20 Citations (Scopus)

Abstract

Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.

Original language	English
Title of host publication	Proceedings of CBMS 2012
Subtitle of host publication	The 25th IEEE International Symposium on Computer-Based Medical Systems
Editors	Paolo Soda, Francesco Tortorella, Sameer Antani, Mykola Pechenizkiy, Mario Cannataro, Alexey Tsymbal
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781467320511
DOIs	https://doi.org/10.1109/CBMS.2012.6266342
Publication status	Published - 2012
Event	25th IEEE International Symposium on Computer-Based Medical Systems: CBMS 2012 - Universita Campus Bio-Medico di Roma, Rome, Italy Duration: 20 Jun 2012 → 22 Jun 2012 https://web.archive.org/web/20120501142858/http://www.cbms2012.org/ (Conference website)

Conference

Conference	25th IEEE International Symposium on Computer-Based Medical Systems
Country/Territory	Italy
City	Rome
Period	20/06/12 → 22/06/12
Other	CBMS 2012 will provide an international forum for discussing the latest developments in the field of computational medicine, biomedical informatics and related fields. The conference is part of the Bioengineering Week, an exciting week totally dedicated to bioengineering research, featuring a unique concentration of several scientific, cultural and educational events, mostly taking place in Rome.
Internet address	https://web.archive.org/web/20120501142858/http://www.cbms2012.org/ (Conference website)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CBMS.2012.6266342

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Jelinek, H., Pires, R., Padilha, R., Goldenstein, S., Wainer, J., Bossomaier, T., & Rocha, A. (2012). Data Fusion for multi-lesion diabetic retinopathy detection. In P. Soda, F. Tortorella, S. Antani, M. Pechenizkiy, M. Cannataro, & A. Tsymbal (Eds.), Proceedings of CBMS 2012: The 25th IEEE International Symposium on Computer-Based Medical Systems (pp. 1-4). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CBMS.2012.6266342

Jelinek, Herbert ; Pires, R. ; Padilha, R. et al. / Data Fusion for multi-lesion diabetic retinopathy detection. Proceedings of CBMS 2012: The 25th IEEE International Symposium on Computer-Based Medical Systems. editor / Paolo Soda ; Francesco Tortorella ; Sameer Antani ; Mykola Pechenizkiy ; Mario Cannataro ; Alexey Tsymbal. United States : IEEE, Institute of Electrical and Electronics Engineers, 2012. pp. 1-4

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title = "Data Fusion for multi-lesion diabetic retinopathy detection",

abstract = "Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.",

keywords = "Combination method, Complex task, Diabetic retinopathy, Diagnosis problem, Fusion techniques, Points of interest, Unified framework, Visual word",

author = "Herbert Jelinek and R. Pires and R. Padilha and S. Goldenstein and J. Wainer and Terence Bossomaier and A. Rocha",

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doi = "10.1109/CBMS.2012.6266342",

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editor = "Paolo Soda and Francesco Tortorella and Sameer Antani and Mykola Pechenizkiy and Mario Cannataro and Alexey Tsymbal",

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Jelinek, H, Pires, R, Padilha, R, Goldenstein, S, Wainer, J, Bossomaier, T & Rocha, A 2012, Data Fusion for multi-lesion diabetic retinopathy detection. in P Soda, F Tortorella, S Antani, M Pechenizkiy, M Cannataro & A Tsymbal (eds), Proceedings of CBMS 2012: The 25th IEEE International Symposium on Computer-Based Medical Systems. IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-4, 25th IEEE International Symposium on Computer-Based Medical Systems, Rome, Italy, 20/06/12. https://doi.org/10.1109/CBMS.2012.6266342

Data Fusion for multi-lesion diabetic retinopathy detection. / Jelinek, Herbert; Pires, R.; Padilha, R. et al.
Proceedings of CBMS 2012: The 25th IEEE International Symposium on Computer-Based Medical Systems. ed. / Paolo Soda; Francesco Tortorella; Sameer Antani; Mykola Pechenizkiy; Mario Cannataro; Alexey Tsymbal. United States: IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 1-4.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Pires, R.

AU - Padilha, R.

AU - Goldenstein, S.

AU - Wainer, J.

AU - Bossomaier, Terence

AU - Rocha, A.

N1 - Imported on 03 May 2017 - DigiTool details were: 086 FoR could not be migrated (80201 - ). publisher = United States: IEEE, 2012. Event dates (773o) = 20-22 June 2012; Parent title (773t) = IEEE International Symposium on Computer Based Medical Systems (CBMS).

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AB - Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.

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KW - Fusion techniques

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KW - Unified framework

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ER -

Jelinek H, Pires R, Padilha R, Goldenstein S, Wainer J, Bossomaier T et al. Data Fusion for multi-lesion diabetic retinopathy detection. In Soda P, Tortorella F, Antani S, Pechenizkiy M, Cannataro M, Tsymbal A, editors, Proceedings of CBMS 2012: The 25th IEEE International Symposium on Computer-Based Medical Systems. United States: IEEE, Institute of Electrical and Electronics Engineers. 2012. p. 1-4 doi: 10.1109/CBMS.2012.6266342

Data Fusion for multi-lesion diabetic retinopathy detection

Abstract

Conference

UN SDGs

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