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Volumn , Issue , 2008, Pages 189-198

Learning-based detection of stenotic lesions in coronary CT data

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST ALGORITHM; CLINICAL EXPERIENCE; COMPUTED TOMOGRAPHY ANGIOGRAPHY; CORONARY ANGIOGRAPHY; FEATURE EXTRACTION METHODS; MULTISLICE COMPUTED TOMOGRAPHY; RELEVANT INFORMATIONS; STRONG CLASSIFIERS;

EID: 84881586994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (19)
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  • 3
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    • Budoff, M.J.1    Achenbach, S.2    Blumenthal, R.S.3    Carr, J.J.4    Goldin, J.G.5    Greenland, P.6    Guerci, A.D.7    Lima, J.A.C.8    Rader, D.J.9    Rubin, G.D.10    Shaw, L.J.11    Wiegers, S.E.12
  • 4
    • 33748616268 scopus 로고    scopus 로고
    • Localizing calcifications in cardiac CT data sets using a new vessel segmentation approach
    • S. Wesarg, M. F. Khan and E. A. Firle. Localizing Calcifications in Cardiac CT Data Sets Using a New Vessel Segmentation Approach. Journal of Digital Imaging, 19(3), 249-257, 2006
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    • Wesarg, S.1    Khan, M.F.2    Firle, E.A.3
  • 6
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Y. Freund and R. E. Schapire. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55, 119-139, 1997
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 8
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • R. E. Schapire and Y. Singer. Improved Boosting Algorithms Using Confidence-Rated Predictions. Journal of Machine Learning, 37(3), 297-336, 1999
    • (1999) Journal of Machine Learning , vol.37 , Issue.3 , pp. 297-336
    • Schapire, R.E.1    Singer, Y.2
  • 9
    • 50649107002 scopus 로고    scopus 로고
    • Fast automatic multi-chamber heart segmentation from 3D CT data using marginal space learning and steerable features
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    • Y Zheng, A. Barbu, B. Georgescu, M. Scheuering and D. Comaniciu. Fast Automatic Multi-Chamber Heart Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features. In Int. Conf. on Computer Vision, 1-8, 2007
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  • 10
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    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance and min-redundancy
    • H. Peng, F. Long and C. Ding. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance and Min-Redundancy. In Transactions on Pattern Analysis and Machine Intelligence, (27)8:1226-1238, 2005
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    • Peng, H.1    Long, F.2    Ding, C.3
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    • Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms
    • A. Wroblewska, P. Boninski, A. Przelaskowski and M. Kazubek. Segmentation and Feature Extraction for Reliable Classification of Microcalcifications in Digital Mammograms. In Opto-Electronics Review, 11(3):227-235, 2006
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.