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Co-Authors

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

8.198
  1. Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset. J Med Imaging (Bellingham). 2022 May; 9(3):034502.
    View in: PubMed
    Score: 0.933
  2. Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma. Radiology. 2019 04; 291(1):15-20.
    View in: PubMed
    Score: 0.742
  3. Breast density estimation from high spectral and spatial resolution MRI. J Med Imaging (Bellingham). 2016 Oct; 3(4):044507.
    View in: PubMed
    Score: 0.640
  4. 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: 0.613
  5. 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: 0.612
  6. A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features. J Med Imaging (Bellingham). 2022 May; 9(3):034501.
    View in: PubMed
    Score: 0.233
  7. A review of explainable and interpretable AI with applications in COVID-19 imaging. Med Phys. 2022 Jan; 49(1):1-14.
    View in: PubMed
    Score: 0.225
  8. 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: 0.223
  9. Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel). 2021 Sep 26; 13(19).
    View in: PubMed
    Score: 0.222
  10. 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: 0.214
  11. 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: 0.200
  12. 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: 0.193
  13. 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: 0.186
  14. 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: 0.179
  15. 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: 0.179
  16. 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: 0.175
  17. 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: 0.168
  18. 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: 0.168
  19. 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: 0.156
  20. 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: 0.149
  21. 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: 0.138
  22. 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: 0.136
  23. 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: 0.132
  24. 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: 0.119
  25. 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: 0.117
  26. 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: 0.091
  27. 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: 0.086
  28. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007 May; 14(5):513-21.
    View in: PubMed
    Score: 0.082
  29. Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol. 2005 Jul; 12(7):863-73.
    View in: PubMed
    Score: 0.072
  30. 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: 0.066
  31. MIDRC CRP10 AI interface-an integrated tool for exploring, testing and visualization of AI models. Phys Med Biol. 2023 03 23; 68(7).
    View in: PubMed
    Score: 0.062
  32. Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI. Radiology. 2023 04; 307(1):e220984.
    View in: PubMed
    Score: 0.061
  33. 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: 0.048
  34. 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: 0.047
  35. Variation in algorithm implementation across radiomics software. J Med Imaging (Bellingham). 2018 Oct; 5(4):044505.
    View in: PubMed
    Score: 0.046
  36. Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Eur Radiol Exp. 2017; 1(1):22.
    View in: PubMed
    Score: 0.043
  37. 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: 0.041
  38. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 07; 5:17787.
    View in: PubMed
    Score: 0.037
  39. 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: 0.037
  40. 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: 0.033
  41. 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: 0.032
  42. 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: 0.028
  43. 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: 0.027
  44. Normal parenchymal enhancement patterns in women undergoing MR screening of the breast. Eur Radiol. 2011 Jul; 21(7):1374-82.
    View in: PubMed
    Score: 0.027
  45. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol. 2010 Sep; 17(9):1158-67.
    View in: PubMed
    Score: 0.026
  46. 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: 0.026
  47. 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: 0.025
  48. 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: 0.025
  49. Correlative feature analysis on FFDM. Med Phys. 2008 Dec; 35(12):5490-500.
    View in: PubMed
    Score: 0.023
  50. A dual-stage method for lesion segmentation on digital mammograms. Med Phys. 2007 Nov; 34(11):4180-93.
    View in: PubMed
    Score: 0.021
  51. 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: 0.021
  52. 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.