The University of Chicago Header Logo

Connection

Maryellen L. Giger to Bayes Theorem

This is a "connection" page, showing publications Maryellen L. Giger has written about Bayes Theorem.
Connection Strength

0.170
  1. Ideal observer approximation using Bayesian classification neural networks. IEEE Trans Med Imaging. 2001 Sep; 20(9):886-99.
    View in: PubMed
    Score: 0.045
  2. Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions. J Magn Reson Imaging. 2014 Jan; 39(1):59-67.
    View in: PubMed
    Score: 0.026
  3. Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging. 2012 Oct; 25(5):591-8.
    View in: PubMed
    Score: 0.024
  4. A scaling transformation for classifier output based on likelihood ratio: applications to a CAD workstation for diagnosis of breast cancer. Med Phys. 2012 May; 39(5):2787-804.
    View in: PubMed
    Score: 0.023
  5. Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology. 2010 Mar; 254(3):680-90.
    View in: PubMed
    Score: 0.020
  6. Breast US computer-aided diagnosis system: robustness across urban populations in South Korea and the United States. Radiology. 2009 Dec; 253(3):661-71.
    View in: PubMed
    Score: 0.020
  7. Computerized lesion detection on breast ultrasound. Med Phys. 2002 Jul; 29(7):1438-46.
    View in: PubMed
    Score: 0.012
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.