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Connection

Robert Nishikawa to Breast

This is a "connection" page, showing publications Robert Nishikawa has written about Breast.
Connection Strength

1.892
  1. Validation of a power-law noise model for simulating small-scale breast tissue. Phys Med Biol. 2013 Sep 07; 58(17):6011-27.
    View in: PubMed
    Score: 0.386
  2. On the orientation of mammographic structure. Med Phys. 2011 Oct; 38(10):5303-6.
    View in: PubMed
    Score: 0.340
  3. Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. Med Phys. 2010 Apr; 37(4):1591-600.
    View in: PubMed
    Score: 0.306
  4. Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover. Breast Cancer Res. 2024 02 01; 26(1):21.
    View in: PubMed
    Score: 0.200
  5. Developing breast lesion detection algorithms for digital breast tomosynthesis: Leveraging false positive findings. Med Phys. 2022 Dec; 49(12):7596-7608.
    View in: PubMed
    Score: 0.180
  6. Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms. IEEE Trans Med Imaging. 2022 01; 41(1):225-236.
    View in: PubMed
    Score: 0.173
  7. Automated mammographic breast density estimation using a fully convolutional network. Med Phys. 2018 Mar; 45(3):1178-1190.
    View in: PubMed
    Score: 0.132
  8. A statistically defined anthropomorphic software breast phantom. Med Phys. 2012 Jun; 39(6):3375-85.
    View in: PubMed
    Score: 0.089
  9. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology. 2023 06; 307(5):e222639.
    View in: PubMed
    Score: 0.048
  10. Algorithmic scatter correction in dual-energy digital mammography. Med Phys. 2013 Nov; 40(11):111919.
    View in: PubMed
    Score: 0.025
  11. Computerized mass detection for digital breast tomosynthesis directly from the projection images. Med Phys. 2006 Feb; 33(2):482-91.
    View in: PubMed
    Score: 0.014
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.