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Connection

Robert Nishikawa to Pattern Recognition, Automated

This is a "connection" page, showing publications Robert Nishikawa has written about Pattern Recognition, Automated.
  1. CADe for early detection of breast cancer-current status and why we need to continue to explore new approaches. Acad Radiol. 2014 Oct; 21(10):1320-1.
    View in: PubMed
    Score: 0.428
  2. A comparison study of image features between FFDM and film mammogram images. Med Phys. 2012 Jul; 39(7):4386-94.
    View in: PubMed
    Score: 0.370
  3. Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys. 2012 Feb; 39(2):676-85.
    View in: PubMed
    Score: 0.360
  4. The hypervolume under the ROC hypersurface of "near-guessing" and "near-perfect" observers in N-class classification tasks. IEEE Trans Med Imaging. 2005 Mar; 24(3):293-9.
    View in: PubMed
    Score: 0.223
  5. Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Med Phys. 2004 Sep; 31(9):2648-57.
    View in: PubMed
    Score: 0.215
  6. The use of a priori information in the detection of mammographic microcalcifications to improve their classification. Med Phys. 2003 May; 30(5):823-31.
    View in: PubMed
    Score: 0.196
  7. Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys. 2012 Feb; 39(2):866-73.
    View in: PubMed
    Score: 0.090
  8. Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. Med Phys. 2008 Apr; 35(4):1486-93.
    View in: PubMed
    Score: 0.069
  9. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging. 2005 Mar; 24(3):371-80.
    View in: PubMed
    Score: 0.056
  10. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004 Oct; 23(10):1233-44.
    View in: PubMed
    Score: 0.054
  11. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002 Dec; 21(12):1552-63.
    View in: PubMed
    Score: 0.048
  12. Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans Med Imaging. 2005 Oct; 24(10):1278-85.
    View in: PubMed
    Score: 0.015
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.