Hui Li

发表

Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.

Andrew R. Jamieson, M. Giger, K. Drukker, 2009, Medical physics.

Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.

M. Giger, L. Lan, G. Newstead, 2010, Radiology.

Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI.

M. Giger, Hui Li, Yading Yuan, 2010, Academic radiology.

Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images

M. Giger, Weijie Chen, U. Bick, 2007, Magnetic resonance in medicine.

Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma

M. Giger, L. Lan, K. Drukker, 2015, Scientific Reports.

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Erich P Huang, E. Burnside, C. Fan, 2016, npj Breast Cancer.

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

Erich P Huang, E. Burnside, M. Giger, 2016, Cancer.

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.

Erich P Huang, E. Burnside, C. Fan, 2016, Radiology.

Combined use of T2‐weighted MRI and T1‐weighted dynamic contrast–enhanced MRI in the automated analysis of breast lesions

M. Giger, L. Lan, G. Newstead, 2011, Magnetic resonance in medicine.

Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location.

M. Giger, Z. Huo, O. Olopade, 2004, Medical physics.

Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution

M. Giger, Hui Li, J. Papaioannou, 2019, Cancer Imaging.

Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma.

Hui Li, Li Lan, Deepa Sheth, 2019, Radiology.

Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment.

Li Lan, Hui Li, Maryellen L Giger, 2007, Academic radiology.

Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival “early on” in neoadjuvant treatment of breast cancer

Karen Drukker, Alexandra Edwards, John Papaioannou, 2018, Cancer Imaging.

Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.

Li Lan, Andrew R. Jamieson, Hui Li, 2014, Medical physics.

Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T

M. Giger, O. Olopade, G. Newstead, 2017, Journal of magnetic resonance imaging : JMRI.

Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification.

Karen Drukker, Karla Kerlikowske, Hui Li, 2014, Medical physics.

Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling

Maryellen L. Giger, Yuan Ji, Karen Drukker, 2019, Cancer Imaging.

Case-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions

Maryellen L. Giger, Karen Drukker, Hiroyuki Abe, 2021, Medical Imaging.

Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers

M. Giger, Hui Li, Peifang Liu, 2022, Medical Imaging.

Association between DCE MRI background parenchymal enhancement and mammographic texture features

Natalie M. Baughan, M. Giger, L. Lan, 2022, Medical Imaging.

A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features

M. Giger, T. Fahey, L. Lan, 2022, Journal of medical imaging.

Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset

M. Giger, Hui Li, J. Papaioannou, 2022, Journal of medical imaging.

Breast cancer

M. Giger, Hui Li, 2019, Radiomics and Radiogenomics.

TU‐D‐330A‐03: Comparison of Image Segmentation Algorithms On Digitized Mammograms and FFDM Images for CAD

Andrew R. Jamieson, M. Giger, Hui Li, 2006 .

TU-AB-BRA-07: Radiomics of Breast Cancer: A Robustness Study

M. Giger, N. Antropova, L. Lan, 2015 .

Normal parenchymal enhancement patterns in women undergoing MR screening of the breast

Maryellen L. Giger, Hui Li, Gillian M. Newstead, 2011, European Radiology.

Texture analysis of peripheral bone densitometry images to aid the assessment of risk for fracture

Maryellen L. Giger, Hui Li, Michael R. Chinander, 2004, CARS.

A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer

Hui Li, Z. Ye, Tingting Ma, 2022, Frontiers in Genetics.

Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI.

M. Giger, Hui Li, Peifang Liu, 2023, Radiology.

A review of explainable and interpretable AI with applications in COVID‐19 imaging

Jordan D. Fuhrman, M. Giger, Hui Li, 2021, Medical physics.

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2mutation status: a cross-sectional study

Hui Li, Li Lan, Claudia Giambartolomei, 2014, Breast Cancer Research.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

Hui Li, Benjamin Q Huynh, Maryellen L Giger, 2016, Journal of medical imaging.

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Jordan D. Fuhrman, M. Giger, Weijie Chen, 2021, Journal of medical imaging.

Artificial intelligence and interpretations in breast cancer imaging

M. Giger, Hui Li, 2021 .

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.

M. Giger, L. Lan, Hui Li, 2019, Quantitative imaging in medicine and surgery.

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

Maryellen L. Giger, Karen Drukker, Gary J. Whitman, 2017, European Radiology Experimental.

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls

Li Lan, Hui Li, Maryellen L Giger, 2014, Journal of medical imaging.

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2mutation status: a cross-sectional study

Hui Li, Li Lan, Claudia Giambartolomei, 2014, Breast Cancer Research.

Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers.

Li Lan, Ulrich Bick, Weijie Chen, 2010, Academic radiology.

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

Maryellen L. Giger, Elizabeth Burnside, Karen Drukker, 2015, Journal of medical imaging.

Quantitative MRI Phenotyping of Breast Cancer across Molecular Classification Subtypes

Maryellen L. Giger, Hiroyuki Abe, Hui Li, 2014, Digital Mammography / IWDM.

Computerized three-class classification of MRI-based prognostic markers for breast cancer

Maryellen Giger, Darrin C. Edwards, Neha Bhooshan, 2011, Physics in medicine and biology.

Multi-Stage Harmonization for Robust AI across Breast MR Databases

M. Giger, Hui Li, Peifang Liu, 2021, Cancers.

MIDRC CRP10 AI interface—an integrated tool for exploring, testing and visualization of AI models

Jordan D. Fuhrman, M. Giger, Hui Li, 2023, Physics in medicine and biology.

Harmonization of radiomic features of breast lesions across international DCE-MRI datasets

Maryellen L. Giger, Heather M. Whitney, Peifang Liu, 2020, Journal of medical imaging.

Computerized Texture Analysis of Mammographic Parenchymal Patterns of Digitized Mammograms1

Maryellen L. Giger, Hui Li, Olufunmilayo I. Olopade, 2005 .

Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms

Maryellen L. Giger, Hui Li, Olufunmilayo I. Olopade, 2004, CARS.

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

Maryellen L. Giger, Hui Li, Olufunmilayo I. Olopade, 2012, Journal of Digital Imaging.

Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment

Maryellen L. Giger, Hui Li, Olufunmilayo I. Olopade, 2008, Journal of Digital Imaging.

Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis

Maryellen L. Giger, Hui Li, Kayla R. Mendel, 2018, Medical Imaging.

Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.

Li Lan, Yading Yuan, Andrew R. Jamieson, 2008, Academic radiology.

Performance of CADx on a Large Clinical Database of FFDM Images

Maryellen L. Giger, Hui Li, Li Lan, 2008, Digital Mammography / IWDM.

Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM.

Maryellen Giger, Hui Li, Li Lan, 2019, Medical physics.

Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method

Maryellen L. Giger, Heather M. Whitney, Peifang Liu, 2020, Proceedings of the IEEE.

Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI.

M. Giger, Hui Li, Peifang Liu, 2021, Radiology. Artificial intelligence.

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms

Hui Li, Benjamin Q Huynh, Maryellen L Giger, 2017, Journal of medical imaging.

Assessing an AI-based smart imagery framing and truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest x-ray images for development of deep learning models

Jordan D. Fuhrman, M. Giger, Y. Lure, 2023, Medical Imaging.

Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks.

Hui Li, Natalia Antropova, Maryellen L Giger, 2018, Journal of medical imaging.

Comparison of radiographic texture analysis from computed radiography and bone densitometry systems.

Hui Li, Maryellen L Giger, Vit Jaros, 2004, Medical physics.

A dual-stage method for lesion segmentation on digital mammograms.

Kenji Suzuki, Yading Yuan, Hui Li, 2007, Medical physics.

Grid-Enabled Quantitative Analysis of Breast Cancer

Maryellen L. Giger, Karen Drukker, Hui Li, 2009 .

Variation in algorithm implementation across radiomics software

Hania Al-Hallaq, Maryellen L Giger, Samuel G Armato, 2018, Journal of medical imaging.

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

Hui Li, Deepa Sheth, Maryellen Giger, 2019, Academic radiology.

Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment

Maryellen L. Giger, Hui Li, Kayla R. Mendel, 2019, Medical Imaging.

Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer

Maryellen L. Giger, Yu Ji, Peifang Liu, 2019, Medical Imaging.

Correlative feature analysis on FFDM.

Yading Yuan, Hui Li, Maryellen L Giger, 2008, Medical physics.

Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2,397 breast lesions

Maryellen L. Giger, Heather M. Whitney, Alexandra Edwards, 2019, Medical Imaging.

Identifying Corresponding Lesions from CC and MLO Views Via Correlative Feature Analysis

Maryellen L. Giger, Hui Li, Li Lan, 2008, Digital Mammography / IWDM.

A two-stage method for lesion segmentation on digital mammograms

Maryellen L. Giger, Kenji Suzuki, Hui Li, 2006, SPIE Medical Imaging.

Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction

Natalie M. Baughan, M. Giger, L. Lan, 2023, Cancers.

Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers’ systems

Hiroyuki Abe, Li Lan, Hui Li, 2017, Journal of medical imaging.

Potential of computer‐aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions

Li Lan, Maryellen Giger, Neha Bhooshan, 2014, Journal of magnetic resonance imaging : JMRI.

Role of machine learning in differentiating benign from malignant indeterminate thyroid nodules: a literature review

M. Giger, L. Lan, Hui Li, 2023, Health Sciences Review.

Breast density estimation from high spectral and spatial resolution MRI

Hiroyuki Abe, Hui Li, Maryellen L Giger, 2016, Journal of medical imaging.

Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

Maryellen L. Giger, Hui Li, Kayla R. Mendel, 2016, SPIE Medical Imaging.

User experience evaluation for MIDRC AI interface

Jordan D. Fuhrman, M. Giger, Hui Li, 2023, Medical Imaging.

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography

Jordan D. Fuhrman, M. Giger, Hui Li, 2023, Journal of medical imaging.

Longitudinal robustness of a thoracic radiographic AI model for COVID-19 severity prediction

M. Giger, K. Drukker, Hui Li, 2023, Medical Imaging.

Investigation of demographic implicit discrimination and disparate impact in chest radiography image-based AI for COVID-19 severity prediction

M. Giger, K. Drukker, Hui Li, 2023, Medical Imaging.

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect

Natalie M. Baughan, M. Giger, L. Lan, 2023, Journal of medical imaging.

Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound

M. Giger, L. Lan, Hui Li, 2023, Investigative and clinical urology.

Novel integration of radiomics and deep transfer learning for diagnosis of indeterminate thyroid nodules on ultrasound

M. Giger, L. Lan, Hui Li, 2023, Medical Imaging.

Performance of Triple-Modality CADx on Breast Cancer Diagnostic Classification

Maryellen L. Giger, Karen Drukker, Hui Li, 2010, Digital Mammography / IWDM.

Computerized breast parenchymal analysis on DCE-MRI

Maryellen L. Giger, Hui Li, Li Lan, 2009, Medical Imaging.