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Connection

Hui Li to Radiographic Image Interpretation, Computer-Assisted

This is a "connection" page, showing publications Hui Li has written about Radiographic Image Interpretation, Computer-Assisted.
  1. 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: 0.333
  2. Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J Digit Imaging. 2008 Jun; 21(2):145-52.
    View in: PubMed
    Score: 0.239
  3. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007 May; 14(5):513-21.
    View in: PubMed
    Score: 0.228
  4. Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol. 2005 Jul; 12(7):863-73.
    View in: PubMed
    Score: 0.201
  5. Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM. Med Phys. 2019 May; 46(5):2145-2156.
    View in: PubMed
    Score: 0.130
  6. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography. Acad Radiol. 2019 06; 26(6):735-743.
    View in: PubMed
    Score: 0.125
  7. Computerized three-class classification of MRI-based prognostic markers for breast cancer. Phys Med Biol. 2011 Sep 21; 56(18):5995-6008.
    View in: PubMed
    Score: 0.077
  8. Comparison of radiographic texture analysis from computed radiography and bone densitometry systems. Med Phys. 2004 Apr; 31(4):882-91.
    View in: PubMed
    Score: 0.046
  9. Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification. Med Phys. 2014 Mar; 41(3):031915.
    View in: PubMed
    Score: 0.023
  10. Correlative feature analysis on FFDM. Med Phys. 2008 Dec; 35(12):5490-500.
    View in: PubMed
    Score: 0.016
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.