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Found 191 results
Filters: Author is Rubin, D. L.  [Clear All Filters]
2020
Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup, Swinburne, N. C., Mendelson D., and Rubin D. L. , J Digit Imaging, 02, Volume 33, Number 1, p.49-53, (2020)
Alcohol and Tobacco Use in Relation to Mammographic Density in 23,456 Women, McBride, R. B., Fei K., Rothstein J. H., Alexeeff S. E., Song X., Sakoda L. C., McGuire V., Achacoso N., Acton L., Liang R. Y., et al. , Cancer Epidemiol Biomarkers Prev, May, Volume 29, Number 5, p.1039-1048, (2020)
Automated Quantitative Imaging Measurements of Disease Severity in Patients with Nonthrombotic Iliac Vein Compression, Reposar, A. L., Mabud T. S., Eifler A. C., Hoogi A., Arendt V., Cohn D. M., Rubin D. L., and Hofmann L. V. , J Vasc Interv Radiol, Feb, Volume 31, Number 2, p.270-275, (2020)
Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies, Luque, E. F., Miranda N., Rubin D. L., and Moreira D. A. , J Digit Imaging, Apr, Volume 33, Number 2, p.287-303, (2020)
Cancer Treatment Classification with Electronic Medical Health Records, Zeng, J., Banerjee I., Gensheimer M. F., and Rubin D. L. , (2020)
Cross-Modal Data Programming Enables Rapid Medical Machine Learning, Dunnmon, J. A., Ratner A. J., Saab K., Khandwala N., Markert M., Sagreiya H., Goldman R., Lee-Messer C., Lungren M. P., Rubin D. L., et al. , Patterns (N Y), May 8, Volume 1, Number 2, (2020)
Determinants of cone- and rod-function in geographic atrophy: AI-based structure-function correlation, Pfau, M., von der Emde L., Dysli C., Möller P. T., Thiele S., Lindner M., Schmid M., Rubin D. L., Fleckenstein M., Holz F. G., et al. , Am J Ophthalmol, 04, (2020)
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms, Schaffter, T., Buist D. S. M., Lee C. I., Nikulin Y., Ribli D., Guan Y., Lotter W., Jie Z., Du H., Wang S., et al. , JAMA Netw Open, Mar 2, Volume 3, Number 3, p.e200265, (2020)
Lower Extremity Venous Stent Placement: A Large Retrospective Single-Center Analysis, Mabud, T. S., Cohn D. M., Arendt V. A., Jeon G. S., An X., Fu J., Souffrant A. D., Sailer A. M., Shah R., Wang D., et al. , J Vasc Interv Radiol, Feb, Volume 31, Number 2, p.251-259 e2, (2020)
Natural Language Generation Model for mammography reports simulation, Hoogi, A., Mishra A., Gimenez F., Dong J., and Rubin D. L. , IEEE J Biomed Health Inform, Apr, (2020)
A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors, Durot, I., Akhbardeh A., Sagreiya H., Loening A. M., and Rubin D. L. , Ultrasound Med Biol, Jan, Volume 46, Number 1, p.26-33, (2020)
Preparing Medical Imaging Data for Machine Learning, Willemink, M. J., Koszek W. A., Hardell C., Wu J., Fleischmann D., Harvey H., Folio L. R., Summers R. M., Rubin D. L., and Lungren M. P. , Radiology, 04, Volume 295, Number 1, p.4-15, (2020)
Quantitative Framework for Risk Stratification of Thyroid Nodules With Ultrasound: A Step Toward Automated Triage of Thyroid Cancer, Galimzianova, A., Siebert S. M., Kamaya A., Rubin D. L., and Desser T. S. , AJR Am J Roentgenol, 04, Volume 214, Number 4, p.885-892, (2020)
Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines, Mattonen, S. A., Gude D., Echegaray S., Bakr S., Rubin D. L., and Napel S. , J Med Imaging (Bellingham), Jul, Volume 7, Number 4, p.042803, (2020)
Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response, A. Kaffas, El, Hoogi A., Zhou J., Durot I., Wang H., Rosenberg J., Tseng A., Sagreiya H., Akhbardeh A., Rubin D. L., et al. , Sci Rep, Apr 24, Volume 10, Number 1, p.6996, (2020)
Toward Data-Driven Learning Healthcare Systems in Interventional Radiology: Implementation to Evaluate Venous Stent Patency, Cohn, D. M., Mabud T. S., Arendt V. A., Souffrant A. D., Jeon G. S., An X., Kuo W. T., Sze D. Y., Hofmann L. V., and Rubin D. L. , J Digit Imaging, Feb, Volume 33, Number 1, p.25-36, (2020)
2019
Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup, Swinburne, N. C., Mendelson D., and Rubin D. L. , J Digit Imaging, Feb 25, (2019)
Artificial Intelligence in Imaging: The Radiologist's Role, Rubin, D. L. , J Am Coll Radiol, Sep, Volume 16, Number 9 Pt B, p.1309-1317, (2019)
Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs, Dunnmon, J. A., Yi D., Langlotz C. P., Re C., Rubin D. L., and Lungren M. P. , Radiology, Feb, Volume 290, Number 2, p.537-544, (2019)
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm, Bozkurt, S., Alkim E., Banerjee I., and Rubin D. L. , J Digit Imaging, Aug, Volume 32, Number 4, p.544-553, (2019)
Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data, Gensheimer, M. F., Henry A. S., Wood D. J., Hastie T. J., Aggarwal S., Dudley S. A., Pradhan P., Banerjee I., Cho E., Ramchandran K., et al. , J Natl Cancer Inst, Jun, Volume 111, Number 6, p.568-574, (2019)
Automatic inference of BI-RADS final assessment categories from narrative mammography report findings, Banerjee, I., Bozkurt S., Alkim E., Sagreiya H., Kurian A. W., and Rubin D. L. , J Biomed Inform, Apr, Volume 92, p.103137, (2019)
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification, Banerjee, I., Ling Y., Chen M. C., Hasan S. A., Langlotz C. P., Moradzadeh N., Chapman B., Amrhein T., Mong D., Rubin D. L., et al. , Artif Intell Med, Jun, Volume 97, p.79-88, (2019)
Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support, Banerjee, I., Sofela M., Yang J., Chen J. H., Shah N. H., Ball R., Mushlin A. I., Desai M., Bledsoe J., Amrhein T., et al. , JAMA Netw Open, Aug 2, Volume 2, Number 8, p.e198719, (2019)
ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging, Rubin, D. L., M. Akdogan Ugur, Altindag C., and Alkim E. , Tomography, Mar, Volume 5, Number 1, p.170-183, (2019)
Imaging, Genetic, and Demographic Factors Associated With Conversion to Neovascular Age-Related Macular Degeneration: Secondary Analysis of a Randomized Clinical Trial, Hallak, J. A., de Sisternes L., Osborne A., Yaspan B., Rubin D. L., and Leng T. , JAMA Ophthalmol, Jul 1, Volume 137, Number 7, p.738-744, (2019)
Improving Sample Complexity with Observational Supervision, Saab, K., Dunnmon J. A., Ratner A. V., Rubin D. L., and Re C. , ICLR LLD Workshop, p.https://openreview.net/forum?id=r1gPtjcH_N, (2019)
Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study, Bozkurt, S., Kan K. M., Ferrari M. K., Rubin D. L., Blayney D. W., Hernandez-Boussard T., and Brooks J. D. , BMJ Open, Jul 18, Volume 9, Number 7, p.e027182, (2019)
A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging, Akhbardeh, A., Sagreiya H., A. Kaffas El, Willmann J. K., and Rubin D. L. , Med Phys, Feb, Volume 46, Number 2, p.590-600, (2019)
A Multi-Scale Deep Convolutional Neural Network for Joint Segmentation and Prediction of Geographic Atrophy in Sd-Oct Images, Zhang, Y. H., Ji Z. X., Niu S. J., Leng T., Rubin D. L., and Chen Q. , 2019 Ieee 16th International Symposium on Biomedical Imaging (Isbi 2019)2019 Ieee 16th International Symposium on Biomedical Imaging (Isbi 2019), p.565-568, (2019)
Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer, Banerjee, I., Bozkurt S., Caswell-Jin J. L., Kurian A. W., and Rubin D. L. , JCO Clin Cancer Inform, Oct, Volume 3, p.1-12, (2019)
Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma, Sagreiya, H., Akhbardeh A., Li D., Sigrist R., Chung B. I., Sonn G. A., Tian L., Rubin D. L., and Willmann J. K. , Ultrasound Med Biol, 08, Volume 45, Number 8, p.1944-1954, (2019)
A Probabilistic Model to Support Radiologists' Classification Decisions in Mammography Practice, Zeng, J., Gimenez F., Burnside E. S., Rubin D. L., and Shachter R. , Med Decis Making, Apr, Volume 39, Number 3, p.208-216, (2019)
Reproductive Factors and Mammographic Density: Associations Among 24,840 Women and Comparison of Studies Using Digitized Film-Screen Mammography and Full-Field Digital Mammography, Alexeeff, S. E., Odo N. U., McBride R., McGuire V., Achacoso N., Rothstein J. H., Lipson J. A., Liang R. Y., Acton L., Yaffe M. J., et al. , Am J Epidemiol, Jun 1, Volume 188, Number 6, p.1144-1154, (2019)
The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy, Starkov, P., Aguilera T. A., Golden D. I., Shultz D. B., Trakul N., Maxim P. G., Le Q. T., Loo B. W., Diehn M., Depeursinge A., et al. , Br J Radiol, Feb, Volume 92, Number 1094, p.20180228, (2019)
Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment, Banerjee, I., Li K., Seneviratne M., Ferrari M., Seto T., Brooks J. D., Rubin D. L., and Hernandez-Boussard T. , JAMIA Open, Apr, Volume 2, Number 1, p.150-159, (2019)
2018
Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging, Banerjee, I., Malladi S., Lee D., Depeursinge A., Telli M., Lipson J., Golden D., and Rubin D. L. , J Med Imaging (Bellingham), Jan, Volume 5, Number 1, p.011008, (2018)
Automated dendritic spine detection using convolutional neural networks on maximum intensity projected microscopic volumes, Xiao, X., Djurisic M., Hoogi A., Sapp R. W., Shatz C. J., and Rubin D. L. , J Neurosci Methods, Nov 1, Volume 309, p.25-34, (2018)
Automatic information extraction from unstructured mammography reports using distributed semantics, Gupta, A., Banerjee I., and Rubin D. L. , J Biomed Inform, Feb, Volume 78, p.78-86, (2018)
Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images, Ji, Z., Chen Q., Niu S., Leng T., and Rubin D. L. , Transl Vis Sci Technol, Jan, Volume 7, Number 1, p.1, (2018)
Distributed deep learning networks among institutions for medical imaging, Chang, K., Balachandar N., Lam C., Yi D., Brown J., Beers A., Rosen B., Rubin D. L., and Kalpathy-Cramer J. , J Am Med Inform Assoc, Aug 1, Volume 25, Number 8, p.945-954, (2018)
Integrative Personal Omics Profiles during Periods of Weight Gain and Loss, Piening, B. D., Zhou W., Contrepois K., Rost H., Urban G. J. Gu, Mishra T., Hanson B. M., Bautista E. J., Leopold S., Yeh C. Y., et al. , Cell Syst, Feb 28, Volume 6, Number 2, p.157-170 e8, (2018)
Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy, Wu, J., Cao G., Sun X., Lee J., Rubin D. L., Napel S., Kurian A. W., Daniel B. L., and Li R. , Radiology, Jul, Volume 288, Number 1, p.26-35, (2018)
Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions, Galimzianova, A., Lesjak Z., Rubin D. L., Likar B., Pernus F., and Spiclin Z. , J Med Imaging (Bellingham), Jan, Volume 5, Number 1, p.011007, (2018)
The LOINC RSNA radiology playbook - a unified terminology for radiology procedures, Vreeman, D. J., Abhyankar S., Wang K. C., Carr C., Collins B., Rubin D. L., and Langlotz C. P. , J Am Med Inform Assoc, Jul 1, Volume 25, Number 7, p.885-893, (2018)
Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer, Wu, J., Li X., Teng X., Rubin D. L., Napel S., Daniel B. L., and Li R. , Breast Cancer Res, Sep 3, Volume 20, Number 1, p.101, (2018)
A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI, Cheng, M., Galimzianova A., Lesjak Z., Spiclin Z., Lock C. B., and Rubin D. L. , Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Dlmia 2018Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Dlmia 2018, Volume 11045, p.353-360, (2018)
Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications, Zhou, M., Leung A., Echegaray S., Gentles A., Shrager J. B., Jensen K. C., Berry G. J., Plevritis S. K., Rubin D. L., Napel S., et al. , Radiology, Jan, Volume 286, Number 1, p.307-315, (2018)
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives, Banerjee, I., Gensheimer M. F., Wood D. J., Henry S., Aggarwal S., Chang D. T., and Rubin D. L. , Sci Rep, Jul 3, Volume 8, Number 1, p.10037, (2018)
Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports, Bulu, H., Sippo D. A., Lee J. M., Burnside E. S., and Rubin D. L. , J Digit Imaging, Oct, Volume 31, Number 5, p.596-603, (2018)
Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images, Echegaray, S., Bakr S., Rubin D. L., and Napel S. , J Digit Imaging, Aug, Volume 31, Number 4, p.403-414, (2018)
A radiogenomic dataset of non-small cell lung cancer, Bakr, S., Gevaert O., Echegaray S., Ayers K., Zhou M., Shafiq M., Zheng H., Benson J. A., Zhang W., Leung A. N. C., et al. , Sci Data, Oct 16, Volume 5, p.180202, (2018)
Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort, Banerjee, I., Chen M. C., Lungren M. P., and Rubin D. L. , J Biomed Inform, Jan, Volume 77, p.11-20, (2018)
Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs, Banerjee, I., Kurtz C., Devorah A. E., Do B., Rubin D. L., and Beaulieu C. F. , J Biomed Inform, Aug, Volume 84, p.123-135, (2018)
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization, Banerjee, I., Choi H. H., Desser T., and Rubin D. L. , AMIA Annu Symp Proc, Volume 2018, p.215-224, (2018)
Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma, Banerjee, I., Crawley A., Bhethanabotla M., Daldrup-Link H. E., and Rubin D. L. , Comput Med Imaging Graph, Apr, Volume 65, p.167-175, (2018)
Unraveling the molecular basis of lung adenocarcinoma dedifferentiation and prognosis by integrating omics and histopathology, Yu, K. H., Berry G. J., Rubin D. L., Re C., Altman R., and Snyder M. , (2018)
The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective, Press, R. H., Shu H. G., Shim H., Mountz J. M., Kurland B. F., Wahl R. L., Jones E. F., Hylton N. M., Gerstner E. R., Nordstrom R. J., et al. , Int J Radiat Oncol Biol Phys, Nov 15, Volume 102, Number 4, p.1219-1235, (2018)
2017
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis, Hoogi, A., Subramaniam A., Veerapaneni R., and Rubin D. L. , IEEE Trans Med Imaging, Mar, Volume 36, Number 3, p.781-791, (2017)
Adaptive local window for level set segmentation of CT and MRI liver lesions, Hoogi, A., Beaulieu C. F., Cunha G. M., Heba E., Sirlin C. B., Napel S., and Rubin D. L. , Med Image Anal, Apr, Volume 37, p.46-55, (2017)
Age at Menarche and Late Adolescent Adiposity Associated with Mammographic Density on Processed Digital Mammograms in 24,840 Women, Alexeeff, S. E., Odo N. U., Lipson J. A., Achacosol N., Rothstein J. H., Yaffe M. J., Liang R. Y., Acton L., McGuire V., Whittemore A. S., et al. , Cancer Epidemiology Biomarkers & PreventionCancer Epidemiology Biomarkers & Prevention, Sep, Volume 26, Number 9, p.1450-1458, (2017)
Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma, Yu, K. H., Berry G. J., Rubin D. L., Re C., Altman R. B., and Snyder M. , Cell Syst, Dec 27, Volume 5, Number 6, p.620-627 e3, (2017)
Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps, Niu, S., Chen Q., de Sisternes L., Leng T., and Rubin D. L. , Med Phys, Dec, Volume 44, Number 12, p.6390-6403, (2017)
Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes, de Sisternes, L., Jonna G., Moss J., Marmor M. F., Leng T., and Rubin D. L. , Biomed Opt Express, Mar 01, Volume 8, Number 3, p.1926-1949, (2017)
Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS, Jeffers, A. M., Sieh W., Lipson J. A., Rothstein J. H., McGuire V., Whittemore A. S., and Rubin D. L. , Radiology, Feb, Volume 282, Number 2, p.348-355, (2017)
Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images, Hwang, K. H., Lee H., Koh G., Willrett D., and Rubin D. L. , J Digit Imaging, Feb, Volume 30, Number 1, p.4-10, (2017)
Common Data Elements in Radiology, Rubin, D. L., and Kahn, Jr. C. E. , Radiology, Jun, Volume 283, Number 3, p.837-844, (2017)
Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions, Banerjee, I., Beaulieu C. F., and Rubin D. L. , J Digit Imaging, Aug, Volume 30, Number 4, p.506-518, (2017)
A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound, Lekadir, K., Galimzianova A., Betriu A., M. Vila Del Mar, Igual L., Rubin D. L., Fernandez E., Radeva P., and Napel S. , IEEE J Biomed Health Inform, Jan, Volume 21, Number 1, p.48-55, (2017)
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions, Akkus, Z., Galimzianova A., Hoogi A., Rubin D. L., and Erickson B. J. , J Digit Imaging, Aug, Volume 30, Number 4, p.449-459, (2017)
Dynamic strategy for personalized medicine: An application to metastatic breast cancer, Chen, X., Shachter R. D., Kurian A. W., and Rubin D. L. , J Biomed Inform, Apr, Volume 68, p.50-57, (2017)
Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer, Wu, J., Li B. L., Sun X. L., Cao G. H., Rubin D. L., Napel S., Ikeda D. M., Kurian A. W., and Li R. J. , RadiologyRadiology, Nov, Volume 285, Number 2, p.401-413, (2017)
Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images, de Sisternes, L., Jonna G., Greven M. A., Chen Q., Leng T., and Rubin D. L. , Transl Vis Sci Technol, Feb, Volume 6, Number 1, p.12, (2017)
Inferring Generative Model Structure with Static Analysis, Varma, P., He B., Bajaj P., Banerjee I., Khandwala N., Rubin D. L., and Re C. , Adv Neural Inf Process Syst, Dec, Volume 30, p.239-249, (2017)
Intelligent Word Embeddings of Free-Text Radiology Reports, Banerjee, I., Madhavan S., Goldman R. E., and Rubin D. L. , AMIA Annu Symp Proc, Volume 2017, p.411-420, (2017)
Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment, Liu, T. T., Achrol A. S., Mitchell L. A., Rodriguez S. A., Feroze A., Iv M., Kim C., Chaudhary N., Gevaert O., Stuart J. M., et al. , Neuro Oncol, Jul 1, Volume 19, Number 7, p.997-1007, (2017)
Piecewise convexity of artificial neural networks, Rister, B., and Rubin D. L. , Neural Netw, Jul 03, Volume 94, p.34-45, (2017)
Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative (18)F FDG-PET/CT metrics, Minamimoto, R., Jamali M., Gevaert O., Echegaray S., Khuong A., Hoang C. D., Shrager J. B., Plevritis S. K., Rubin D. L., Leung A. N., et al. , Oncotarget, Aug 8, Volume 8, Number 32, p.52792-52801, (2017)
Predictive radiogenomics modeling of EGFR mutation status in lung cancer, Gevaert, O., Echegaray S., Khuong A., Hoang C. D., Shrager J. B., Jensen K. C., Berry G. J., Guo H. H., Lau C., Plevritis S. K., et al. , Sci Rep, Jan 31, Volume 7, p.41674, (2017)
Revealing cancer subtypes with higher-order correlations applied to imaging and omics data, Graim, K., Liu T. T., Achrol A. S., Paull E. O., Newton Y., Chang S. D., Harsh G. R. th, Cordero S. P., Rubin D. L., and Stuart J. M. , BMC Med Genomics, Mar 31, Volume 10, Number 1, p.20, (2017)
Robust noise region-based active contour model via local similarity factor for image segmentation, Niu, S. J., Chen Q., de Sisternes L., Ji Z. X., Zhou Z. M., and Rubin D. L. , Pattern RecognitionPattern Recognition, Jan, Volume 61, p.104-119, (2017)
Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound, Galimzianova, A., Siebert S. M., Kamaya A., Desser T. S., and Rubin D. L. , AMIA Annu Symp Proc, Volume 2017, p.734-741, (2017)
Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma, Banerjee, I., Crawley A., Bhethanabotla M., Daldrup-Link H. E., and Rubin D. L. , Comput Med Imaging Graph, May 05, (2017)
Use of Radiology Procedure Codes in Health Care: The Need for Standardization and Structure, Wang, K. C., Patel J. B., Vyas B., Toland M., Collins B., Vreeman D. J., Abhyankar S., Siegel E. L., Rubin D. L., and Langlotz C. P. , Radiographics, Jul-Aug, Volume 37, Number 4, p.1099-1110, (2017)
Volumetric Image Registration From Invariant Keypoints, Rister, B., Horowitz M. A., and Rubin D. L. , IEEE Trans Image Process, Oct, Volume 26, Number 10, p.4900-4910, (2017)
2016
Analysis of Inner and Outer Retinal Thickness in Patients Using Hydroxychloroquine Prior to Development of Retinopathy, de Sisternes, L., Hu J., Rubin D. L., and Marmor M. F. , JAMA Ophthalmol, Mar 17, (2016)
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles, Barker, J., Hoogi A., Depeursinge A., and Rubin D. L. , Med Image Anal, May, Volume 30, p.60-71, (2016)
Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor, Niu, S., de Sisternes L., Chen Q., Leng T., and Rubin D. L. , Biomed Opt Express, Feb 1, Volume 7, Number 2, p.581-600, (2016)
Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS, Jeffers, A. M., Sieh W., Lipson J. A., Rothstein J. H., McGuire V., Whittemore A. S., and Rubin D. L. , Radiology, Sep 5, p.152062, (2016)
Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images, Hwang, K. H., Lee H., Koh G., Willrett D., and Rubin D. L. , J Digit Imaging, Oct 26, (2016)
Case-control study of mammographic density and breast cancer risk using processed digital mammograms, Habel, L. A., Lipson J. A., Achacoso N., Rothstein J. H., Yaffe M. J., Liang R. Y., Acton L., McGuire V., Whittemore A. S., Rubin D. L., et al. , Breast Cancer Res, Volume 18, Number 1, p.53, (2016)
A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma, Rao, A., Rao G., Gutman D. A., Flanders A. E., Hwang S. N., Rubin D. L., Colen R. R., Zinn P. O., Jain R., Wintermark M., et al. , J Neurosurg, Apr, Volume 124, Number 4, p.1008-17, (2016)
Common Data Elements in Radiology, Rubin, D. L., and Kahn, Jr. C. E. , Radiology, Nov 10, p.161553, (2016)
Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas, Liu, T. T., Achrol A. S., Mitchell L. A., Du W. A., Loya J. J., Rodriguez S. A., Feroze A., Westbroek E. M., Yeom K. W., Stuart J. M., et al. , AJNR Am J Neuroradiol, Apr, Volume 37, Number 4, p.621-8, (2016)
Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of 18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis, Wu, J., Aguilera T., Shultz D., Gudur M., Rubin D. L., Loo, Jr. B. W., Diehn M., and Li R. , Radiology, Apr 5, p.151829, (2016)
Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis, Wu, J., Aguilera T., Shultz D., Gudur M., Rubin D. L., Loo, Jr. B. W., Diehn M., and Li R. , Radiology, Oct, Volume 281, Number 1, p.270-8, (2016)
Fully Automated Prediction of Geographic Atrophy Growth Using Quantitative Spectral-Domain Optical Coherence Tomography Biomarkers, Niu, S., de Sisternes L., Chen Q., Rubin D. L., and Leng T. , Ophthalmology, Aug, Volume 123, Number 8, p.1737-50, (2016)
Intratumor Partitioning of Serial Computed Tomography and FDG Positron Emission Tomography Images Identifies High-Risk Tumor Subregions and Predicts Patterns of Failure in Non-Small Cell Lung Cancer After Radiation Therapy, Wu, J., Gensheimer M. F., Dong X., Rubin D. L., Napel S., Diehn M., Loo, Jr. B. W., and Li R. , Int J Radiat Oncol Biol Phys, Oct 1, Volume 96, Number 2S, p.S100, (2016)
Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment, Liu, T. T., Achrol A. S., Mitchell L. A., Rodriguez S. A., Feroze A.,, Kim C., Chaudhary N., Gevaert O., Stuart J. M., et al. , Neuro Oncol, Dec 22, (2016)
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features, Yu, K. H., Zhang C., Berry G. J., Altman R. B., Re C., Rubin D. L., and Snyder M. , Nat Commun, Volume 7, p.12474, (2016)

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