Hui Li
Cytowane przez
Cytowane przez
Digital mammographic tumor classification using transfer learning from deep convolutional neural networks
BQ Huynh, H Li, ML Giger
Journal of Medical Imaging 3 (3), 034501-034501, 2016
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
H Li, Y Zhu, ES Burnside, K Drukker, KA Hoadley, C Fan, SD Conzen, ...
Radiology 281 (2), 382-391, 2016
Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images
W Chen, ML Giger, H Li, U Bick, GM Newstead
Magnetic Resonance in Medicine: An Official Journal of the International …, 2007
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
H Li, Y Zhu, ES Burnside, E Huang, K Drukker, KA Hoadley, C Fan, ...
NPJ breast cancer 2, 16012, 2016
Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers
N Bhooshan, ML Giger, SA Jansen, H Li, L Lan, GM Newstead
Radiology 254 (3), 680-690, 2010
Using selective withdrawal to coat microparticles
I Cohen, H Li, JL Hougland, M Mrksich, SR Nagel
Science 292 (5515), 265-267, 2001
Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma
Y Zhu, H Li, W Guo, K Drukker, L Lan, ML Giger, Y Ji
Scientific reports 5 (1), 17787, 2015
Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and‐SNE
AR Jamieson, ML Giger, K Drukker, H Li, Y Yuan, N Bhooshan
Medical physics 37 (1), 339-351, 2010
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
W Guo, H Li, Y Zhu, L Lan, S Yang, K Drukker, E Morris, E Burnside, ...
Journal of medical imaging 2 (4), 041007-041007, 2015
Catalytic asymmetric dihydroxylation by gold colloids functionalized with self-assembled monolayers
H Li, YY Luk, M Mrksich
Langmuir 15 (15), 4957-4959, 1999
Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms1
H Li, ML Giger, OI Olopade, A Margolis, L Lan, MR Chinander
Academic Radiology 12 (7), 863-873, 2005
A dual‐stage method for lesion segmentation on digital mammograms
Y Yuan, ML Giger, H Li, K Suzuki, C Sennett
Medical physics 34 (11), 4180-4193, 2007
Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location
H Li, ML Giger, Z Huo, OI Olopade, L Lan, BL Weber, I Bonta
Medical physics 31 (3), 549-555, 2004
Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment
H Li, ML Giger, OI Olopade, L Lan
Academic radiology 14 (5), 513-521, 2007
Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms
H Li, ML Giger, BQ Huynh, NO Antropova
Journal of medical imaging 4 (4), 041304-041304, 2017
Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography
K Mendel, H Li, D Sheth, M Giger
Academic radiology 26 (6), 735-743, 2019
Digital mammography in breast cancer: additive value of radiomics of breast parenchyma
H Li, KR Mendel, L Lan, D Sheth, ML Giger
Radiology 291 (1), 15-20, 2019
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
ES Burnside, K Drukker, H Li, E Bonaccio, M Zuley, M Ganott, JM Net, ...
Cancer 122 (5), 748-757, 2016
Variation in algorithm implementation across radiomics software
JJ Foy, KR Robinson, H Li, ML Giger, H Al-Hallaq, SG Armato III
Journal of medical imaging 5 (4), 044505-044505, 2018
Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI
Y Yuan, ML Giger, H Li, N Bhooshan, CA Sennett
Academic radiology 17 (9), 1158-1167, 2010
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