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Co-Authors

This is a "connection" page, showing publications co-authored by Maryellen L. Giger and Hui Li.

 
Connection Strength
 
 
 
7.763
 
  1. Li H, Mendel KR, Lan L, Sheth D, Giger ML. Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma. Radiology. 2019 04; 291(1):15-20.
    View in: PubMed
    Score: 0.784
  2. Li H, Weiss WA, Medved M, Abe H, Newstead GM, Karczmar GS, Giger ML. Breast density estimation from high spectral and spatial resolution MRI. J Med Imaging (Bellingham). 2016 Oct; 3(4):044507.
    View in: PubMed
    Score: 0.677
  3. Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, Fan C, Conzen SD, Zuley M, Net JM, Sutton E, Whitman GJ, Morris E, Perou CM, Ji Y, Giger ML. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016; 2.
    View in: PubMed
    Score: 0.648
  4. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML. 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: 0.647
  5. Gyawali B, Parsad S, Feinberg BA, Nabhan C. Real-World Evidence and Randomized Studies in the Precision Oncology Era: The Right Balance. JCO Precis Oncol. 2017 Nov; 1:1-5.
    View in: PubMed
    Score: 0.359
  6. Gyawali B, Parsad S, Feinberg BA, Nabhan C. Real-World Evidence and Randomized Studies in the Precision Oncology Era: The Right Balance. JCO Precis Oncol. 2017 Nov; 1:1-5.
    View in: PubMed
    Score: 0.359
  7. Gyawali B, Parsad S, Feinberg BA, Nabhan C. Real-World Evidence and Randomized Studies in the Precision Oncology Era: The Right Balance. JCO Precis Oncol. 2017 Nov; 1:1-5.
    View in: PubMed
    Score: 0.359
  8. El Naqa I, Li H, Fuhrman J, Hu Q, Gorre N, Chen W, Giger ML. Lessons learned in transitioning to AI in the medical imaging of COVID-19. J Med Imaging (Bellingham). 2021 Jan; 8(Suppl 1):010902-10902.
    View in: PubMed
    Score: 0.235
  9. Whitney HM, Li H, Ji Y, Liu P, Giger ML. Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel). 2021 Sep 26; 13(19).
    View in: PubMed
    Score: 0.235
  10. Hu Q, Whitney HM, Li H, Ji Y, Liu P, Giger ML. Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI. Radiol Artif Intell. 2021 May; 3(3):e200159.
    View in: PubMed
    Score: 0.226
  11. Whitney HM, Li H, Ji Y, Liu P, Giger ML. Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging (Bellingham). 2020 Jan; 7(1):012707.
    View in: PubMed
    Score: 0.211
  12. Ji Y, Li H, Edwards AV, Papaioannou J, Ma W, Liu P, Giger ML. 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: 0.204
  13. Robinson K, Li H, Lan L, Schacht D, Giger M. 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: 0.197
  14. Mendel K, Li H, Sheth D, Giger M. 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: 0.189
  15. Mendel KR, Li H, Lan L, Cahill CM, Rael V, Abe H, Giger ML. Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems. J Med Imaging (Bellingham). 2018 Jan; 5(1):011002.
    View in: PubMed
    Score: 0.178
  16. Li H, Giger ML, Huynh BQ, Antropova NO. Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J Med Imaging (Bellingham). 2017 Oct; 4(4):041304.
    View in: PubMed
    Score: 0.178
  17. Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham). 2016 Jul; 3(3):034501.
    View in: PubMed
    Score: 0.165
  18. Burnside ES, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton EJ, Brandt KR, Whitman GJ, Conzen SD, Lan L, Ji Y, Zhu Y, Jaffe CC, Huang EP, Freymann JB, Kirby JS, Morris EA, Giger ML. Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer. 2016 Mar 01; 122(5):748-57.
    View in: PubMed
    Score: 0.157
  19. Li H, Giger ML, Lan L, Janardanan J, Sennett CA. Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imaging (Bellingham). 2014 Oct; 1(3):031009.
    View in: PubMed
    Score: 0.146
  20. Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, Prindiville SA, Eng-Wong J, Soballe PW, Giambartolomei C, Mai PL, Galbo CE, Nichols K, Calzone KA, Olopade OI, Gail MH, Giger ML. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res. 2014; 16(4):424.
    View in: PubMed
    Score: 0.144
  21. Li H, Giger ML, Sun C, Ponsukcharoen U, Huo D, Lan L, Olopade OI, Jamieson AR, Brown JB, Di Rienzo A. Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys. 2014 Mar; 41(3):031917.
    View in: PubMed
    Score: 0.139
  22. Li H, Giger ML, Lan L, Bancroft Brown J, MacMahon A, Mussman M, Olopade OI, Sennett C. 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: 0.126
  23. Sadinski M, Giger M, Drukker K, Yamaguchi K, Lan L, Li H. TU-E-217BCD-07: Pilot Study on Consistency in Size Metrics for a Multimodality PEM/MR Breast Imaging Approach. Med Phys. 2012 Jun; 39(6Part24):3915.
    View in: PubMed
    Score: 0.123
  24. Li H, Giger ML, Yuan Y, Chen W, Horsch K, Lan L, Jamieson AR, Sennett CA, Jansen SA. Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol. 2008 Nov; 15(11):1437-45.
    View in: PubMed
    Score: 0.096
  25. Li H, Giger ML, Olopade OI, Chinander MR. 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: 0.091
  26. Li H, Giger ML, Olopade OI, Lan L. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007 May; 14(5):513-21.
    View in: PubMed
    Score: 0.087
  27. Li H, Giger ML, Olopade OI, Margolis A, Lan L, Chinander MR. Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol. 2005 Jul; 12(7):863-73.
    View in: PubMed
    Score: 0.076
  28. Li H, Giger ML, Huo Z, Olopade OI, Lan L, Weber BL, Bonta I. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys. 2004 Mar; 31(3):549-55.
    View in: PubMed
    Score: 0.070
  29. Yeh AC, Li H, Zhu Y, Zhang J, Khramtsova G, Drukker K, Edwards A, McGregor S, Yoshimatsu T, Zheng Y, Niu Q, Abe H, Mueller J, Conzen S, Ji Y, Giger ML, Olopade OI. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019 Jul 15; 19(1):48.
    View in: PubMed
    Score: 0.050
  30. Miller TA, Robinson KR, Li H, Seiwert TY, Haraf DJ, Lan L, Giger ML, Ginat DT. Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma. Quant Imaging Med Surg. 2019 Mar; 9(3):399-408.
    View in: PubMed
    Score: 0.049
  31. Foy JJ, Robinson KR, Li H, Giger ML, Al-Hallaq H, Armato SG. Variation in algorithm implementation across radiomics software. J Med Imaging (Bellingham). 2018 Oct; 5(4):044505.
    View in: PubMed
    Score: 0.048
  32. Sutton EJ, Huang EP, Drukker K, Burnside ES, Li H, Net JM, Rao A, Whitman GJ, Zuley M, Ganott M, Bonaccio E, Giger ML, Morris EA. Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Eur Radiol Exp. 2017; 1(1):22.
    View in: PubMed
    Score: 0.045
  33. Medved M, Li H, Abe H, Sheth D, Newstead GM, Olopade OI, Giger ML, Karczmar GS. 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: 0.043
  34. Zhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML, Ji Y. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 07; 5:17787.
    View in: PubMed
    Score: 0.039
  35. Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, Morris E, Burnside E, Whitman G, Giger ML, Ji Y. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging (Bellingham). 2015 10; 2(4):041007.
    View in: PubMed
    Score: 0.039
  36. Drukker K, Duewer F, Giger ML, Malkov S, Flowers CI, Joe B, Kerlikowske K, Drukteinis JS, Li H, Shepherd JA. 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: 0.035
  37. Bhooshan N, Giger M, Medved M, Li H, Wood A, Yuan Y, Lan L, Marquez A, Karczmar G, Newstead G. 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: 0.034
  38. Bhooshan N, Giger M, Edwards D, Yuan Y, Jansen S, Li H, Lan L, Sattar H, Newstead G. 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: 0.029
  39. Bhooshan N, Giger M, Lan L, Li H, Marquez A, Shimauchi A, Newstead GM. 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: 0.029
  40. Jansen SA, Lin VC, Giger ML, Li H, Karczmar GS, Newstead GM. Normal parenchymal enhancement patterns in women undergoing MR screening of the breast. Eur Radiol. 2011 Jul; 21(7):1374-82.
    View in: PubMed
    Score: 0.028
  41. Yuan Y, Giger ML, Li H, Bhooshan N, Sennett CA. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol. 2010 Sep; 17(9):1158-67.
    View in: PubMed
    Score: 0.027
  42. Chen W, Giger ML, Newstead GM, Bick U, Jansen SA, Li H, Lan L. 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: 0.027
  43. Bhooshan N, Giger ML, Jansen SA, Li H, Lan L, Newstead GM. 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: 0.026
  44. Jamieson AR, Giger ML, Drukker K, Li H, Yuan Y, Bhooshan N. 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: 0.026
  45. Yuan Y, Giger ML, Li H, Sennett C. Correlative feature analysis on FFDM. Med Phys. 2008 Dec; 35(12):5490-500.
    View in: PubMed
    Score: 0.024
  46. Yuan Y, Giger ML, Li H, Suzuki K, Sennett C. A dual-stage method for lesion segmentation on digital mammograms. Med Phys. 2007 Nov; 34(11):4180-93.
    View in: PubMed
    Score: 0.022
  47. Chen W, Giger ML, Li H, Bick U, Newstead GM. 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: 0.022
  48. Wilkie JR, Giger ML, Chinander MR, Vokes TJ, Li H, Dixon L, Jaros V. Comparison of radiographic texture analysis from computed radiography and bone densitometry systems. Med Phys. 2004 Apr; 31(4):882-91.
    View in: PubMed
    Score: 0.017
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.