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Volumn 4046 LNCS, Issue , 2006, Pages 76-83

Computerized classification can reduce unnecessary biopsies in BI-RADS category 4A lesions

Author keywords

[No Author keywords available]

Indexed keywords

BIOPSY; FEATURE EXTRACTION; MAMMOGRAPHY; SENSITIVITY ANALYSIS;

EID: 33746354038     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11783237_11     Document Type: Conference Paper
Times cited : (8)

References (15)
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    • Roque, A.C.1    Andre, T.C.2
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    • Stratification of mammographic cases by BIRADS category with the assistance of a CAD system
    • Lederman, R., Leichter, I., Novak, B., Bamberger, P., Fields, S., Buchbinder, S.: Stratification of mammographic cases by BIRADS category with the assistance of a CAD system. European Radiology. 13: (2003) 347-353.
    • (2003) European Radiology , vol.13 , pp. 347-353
    • Lederman, R.1    Leichter, I.2    Novak, B.3    Bamberger, P.4    Fields, S.5    Buchbinder, S.6
  • 7
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    • Mammographic features of "early" breast cancer
    • Sickles, E.A.: Mammographic features of "early" breast cancer. Am J. Roengenol. (1984) 143:461-464.
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    • The positive predictive value of mammography
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    • Kopans, D.B.1
  • 9
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    • Image segmentation feature selection and pattern classification for mammographic microcalcifications
    • Fu, J.C., Lee, S.K., Wong, S.T., Yeh, J.Y., Wang, A.H., Wu, H.K.: Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput Med Imaging Graph. (2005) 9:419-29.
    • (2005) Comput Med Imaging Graph , vol.9 , pp. 419-429
    • Fu, J.C.1    Lee, S.K.2    Wong, S.T.3    Yeh, J.Y.4    Wang, A.H.5    Wu, H.K.6
  • 10
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    • Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines
    • Papadopoulos A., Fotiadis, D.I., Likas, A.: Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artif Intell Med. (2005) 34:141-50.
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    • A study on several machine-learning methods for classification of malignant and benign microcalcifications
    • Wei, L., Yang, Y., Nishikawa, R.M., Jiang, Y.: A Study on Several Machine-Learning Methods for Classification of Malignant and Benign microcalcifications. IEEE Trans Med Imaging. (2005) 24:371-380
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    • Wei, L.1    Yang, Y.2    Nishikawa, R.M.3    Jiang, Y.4
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    • The ability of microcalcification descriptors in the BI-RADS 4th edition to stratify the risk of malignancy
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.