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Co-Authors

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

 
Connection Strength
 
 
 
6.927
 
  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: 0.824
  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: 0.711
  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: 0.681
  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: 0.680
  5. 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: 0.237
  6. 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: 0.222
  7. 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: 0.215
  8. 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: 0.207
  9. Antropova N, Huynh B, Li H, Giger ML. Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks. J Med Imaging (Bellingham). 2019 Jan; 6(1):011002.
    View in: PubMed
    Score: 0.199
  10. 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: 0.198
  11. Drukker K, Li H, Antropova N, Edwards A, Papaioannou J, Giger ML. Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer. Cancer Imaging. 2018 Apr 13; 18(1):12.
    View in: PubMed
    Score: 0.194
  12. 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: 0.187
  13. 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: 0.187
  14. 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: 0.173
  15. 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: 0.165
  16. 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: 0.153
  17. 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: 0.151
  18. 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: 0.146
  19. 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: 0.132
  20. 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: 0.129
  21. 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: 0.101
  22. 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: 0.095
  23. 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: 0.091
  24. 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: 0.080
  25. 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: 0.073
  26. 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: 0.053
  27. 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: 0.052
  28. 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: 0.051
  29. 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: 0.047
  30. 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: 0.045
  31. 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: 0.041
  32. 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: 0.041
  33. 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: 0.037
  34. 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: 0.035
  35. 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: 0.031
  36. 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: 0.030
  37. 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: 0.030
  38. 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: 0.029
  39. 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: 0.028
  40. 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: 0.028
  41. 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: 0.027
  42. 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: 0.025
  43. 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: 0.024
  44. 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: 0.023
  45. 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: 0.018
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.