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Connection

Maryellen Giger to Image Interpretation, Computer-Assisted

This is a "connection" page, showing publications Maryellen Giger has written about Image Interpretation, Computer-Assisted.
  1. Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound. Int J Comput Assist Radiol Surg. 2025 Feb; 20(2):259-267.
    View in: PubMed
    Score: 0.639
  2. Clinical Artificial Intelligence Applications: Breast Imaging. Radiol Clin North Am. 2021 Nov; 59(6):1027-1043.
    View in: PubMed
    Score: 0.512
  3. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging. 2019 Sep 18; 19(1):64.
    View in: PubMed
    Score: 0.442
  4. Repeatability in computer-aided diagnosis: application to breast cancer diagnosis on sonography. Med Phys. 2010 Jun; 37(6):2659-69.
    View in: PubMed
    Score: 0.232
  5. Computer-aided diagnosis in medical imaging. IEEE Trans Med Imaging. 2001 Dec; 20(12):1205-8.
    View in: PubMed
    Score: 0.129
  6. A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep. 2020 06 29; 10(1):10536.
    View in: PubMed
    Score: 0.117
  7. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology. 2016 Nov; 281(2):382-391.
    View in: PubMed
    Score: 0.088
  8. Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study. J Digit Imaging. 1995 May; 8(2):88-94.
    View in: PubMed
    Score: 0.082
  9. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts. Med Phys. 2014 Jan; 41(1):012901.
    View in: PubMed
    Score: 0.074
  10. Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts. Acad Radiol. 2013 Jul; 20(7):847-53.
    View in: PubMed
    Score: 0.071
  11. Evaluating imaging and computer-aided detection and diagnosis devices at the FDA. Acad Radiol. 2012 Apr; 19(4):463-77.
    View in: PubMed
    Score: 0.065
  12. Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions. Magn Reson Med. 2011 Aug; 66(2):555-64.
    View in: PubMed
    Score: 0.062
  13. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol. 2010 Sep; 17(9):1158-67.
    View in: PubMed
    Score: 0.059
  14. Enhancement of breast CADx with unlabeled data. Med Phys. 2010 Aug; 37(8):4155-72.
    View in: PubMed
    Score: 0.059
  15. Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers. Acad Radiol. 2010 Jul; 17(7):822-9.
    View in: PubMed
    Score: 0.058
  16. Pulmonary nodules: computer-aided detection in digital chest images. Radiographics. 1990 Jan; 10(1):41-51.
    View in: PubMed
    Score: 0.056
  17. Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE. Med Phys. 2010 Jan; 37(1):339-51.
    View in: PubMed
    Score: 0.056
  18. A novel hybrid linear/nonlinear classifier for two-class classification: theory, algorithm, and applications. IEEE Trans Med Imaging. 2010 Feb; 29(2):428-41.
    View in: PubMed
    Score: 0.056
  19. Performance of breast ultrasound computer-aided diagnosis: dependence on image selection. Acad Radiol. 2008 Oct; 15(10):1234-45.
    View in: PubMed
    Score: 0.052
  20. Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys. 1988 Mar-Apr; 15(2):158-66.
    View in: PubMed
    Score: 0.050
  21. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys. 2006 Aug; 33(8):2878-87.
    View in: PubMed
    Score: 0.045
  22. A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol. 2006 Jan; 13(1):63-72.
    View in: PubMed
    Score: 0.043
  23. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys. 2004 May; 31(5):1076-82.
    View in: PubMed
    Score: 0.038
  24. Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. Acad Radiol. 2004 Mar; 11(3):272-80.
    View in: PubMed
    Score: 0.038
  25. Computerized analysis of shadowing on breast ultrasound for improved lesion detection. Med Phys. 2003 Jul; 30(7):1833-42.
    View in: PubMed
    Score: 0.036
  26. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol. 2020 Feb 01; 93(1106):20190855.
    View in: PubMed
    Score: 0.028
  27. Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T. J Magn Reson Imaging. 2017 11; 46(5):1341-1348.
    View in: PubMed
    Score: 0.023
  28. Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study. Radiology. 2014 May; 271(2):461-71.
    View in: PubMed
    Score: 0.019
  29. Computerized detection of masses in digital mammograms: investigation of feature-analysis techniques. J Digit Imaging. 1994 Feb; 7(1):18-26.
    View in: PubMed
    Score: 0.019
  30. 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: 0.018
  31. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med. 2007 Sep; 58(3):562-71.
    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.