Daniel L. Rubin
Daniel L. Rubin
Stanford University
No verified email
Cited by
Cited by
Network analysis of intrinsic functional brain connectivity in Alzheimer's disease
K Supekar, V Menon, D Rubin, M Musen, MD Greicius
PLoS computational biology 4 (6), e1000100, 2008
BioPortal: ontologies and integrated data resources at the click of a mouse
NF Noy, NH Shah, PL Whetzel, B Dai, M Dorf, N Griffith, C Jonquet, ...
Nucleic acids research 37 (suppl_2), W170-W173, 2009
Deep learning for brain MRI segmentation: state of the art and future directions
Z Akkus, A Galimzianova, A Hoogi, DL Rubin, BJ Erickson
Journal of digital imaging 30 (4), 449-459, 2017
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
KH Yu, C Zhang, GJ Berry, RB Altman, C Ré, DL Rubin, M Snyder
Nature communications 7 (1), 1-10, 2016
Integrating genotype and phenotype information: an overview of the PharmGKB project
TE Klein, JT Chang, MK Cho, KL Easton, R Fergerson, M Hewett, Z Lin, ...
The pharmacogenomics journal 1 (3), 167-170, 2001
Content-based image retrieval in radiology: current status and future directions
CB Akgül, DL Rubin, S Napel, CF Beaulieu, H Greenspan, B Acar
Journal of digital imaging 24 (2), 208-222, 2011
PharmGKB: the pharmacogenetics knowledge base
M Hewett, DE Oliver, DL Rubin, KL Easton, JM Stuart, RB Altman, ...
Nucleic acids research 30 (1), 163-165, 2002
MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set
DA Gutman, LAD Cooper, SN Hwang, CA Holder, JJ Gao, TD Aurora, ...
Radiology 267 (2), 560-569, 2013
Non–small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results
O Gevaert, J Xu, CD Hoang, AN Leung, Y Xu, A Quon, DL Rubin, S Napel, ...
Radiology 264 (2), 387, 2012
A curated mammography data set for use in computer-aided detection and diagnosis research
RS Lee, F Gimenez, A Hoogi, KK Miyake, M Gorovoy, DL Rubin
Scientific data 4 (1), 1-9, 2017
Biomedical ontologies: a functional perspective
DL Rubin, NH Shah, NF Noy
Briefings in bioinformatics 9 (1), 75-90, 2008
Preparing medical imaging data for machine learning
MJ Willemink, WA Koszek, C Hardell, J Wu, D Fleischmann, H Harvey, ...
Radiology 295 (1), 4-15, 2020
The ACR BI-RADS® experience: learning from history
ES Burnside, EA Sickles, LW Bassett, DL Rubin, CH Lee, DM Ikeda, ...
Journal of the American College of Radiology 6 (12), 851-860, 2009
Differential data augmentation techniques for medical imaging classification tasks
Z Hussain, F Gimenez, D Yi, D Rubin
AMIA annual symposium proceedings 2017, 979, 2017
Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities
H Itakura, AS Achrol, LA Mitchell, JJ Loya, T Liu, EM Westbroek, ...
Science translational medicine 7 (303), 303ra138-303ra138, 2015
Use of Microcalcification Descriptors in BI-RADS 4th Edition to Stratify Risk of Malignancy1
ES Burnside, JE Ochsner, KJ Fowler, JP Fine, LR Salkowski, DL Rubin, ...
Radiology 242 (2), 388-395, 2007
Toward best practices in radiology reporting
CE Kahn Jr, CP Langlotz, ES Burnside, JA Carrino, DS Channin, ...
Radiology 252 (3), 852-856, 2009
Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks
MG Ertosun, DL Rubin
AMIA annual symposium proceedings 2015, 1899, 2015
Distributed deep learning networks among institutions for medical imaging
K Chang, N Balachandar, C Lam, D Yi, J Brown, A Beers, B Rosen, ...
Journal of the American Medical Informatics Association 25 (8), 945-954, 2018
Deep learning in neuroradiology
G Zaharchuk, E Gong, M Wintermark, D Rubin, CP Langlotz
American Journal of Neuroradiology 39 (10), 1776-1784, 2018
The system can't perform the operation now. Try again later.
Articles 1–20