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Publications

Found 273 results
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)
Association of Tumor [(18)F]FDG Activity and Diffusion Restriction with Clinical Outcomes of Rhabdomyosarcomas, A. Lahiji, Pourmehdi, Jackson T., Nejadnik H., von Eyben R., Rubin D., Spunt S. L., Quon A., and Daldrup-Link H. , Mol Imaging Biol, Sep 5, (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)
Deep Learning in Neuroradiology, Zaharchuk, G., Gong E., Wintermark M., Rubin D., and Langlotz C. P. , AJNR Am J Neuroradiol, Oct, Volume 39, Number 10, p.1776-1784, (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)
Expanding a radiology lexicon using contextual patterns in radiology reports, Percha, B., Zhang Y., Bozkurt S., Rubin D., Altman R. B., and Langlotz C. P. , J Am Med Inform Assoc, Jun 1, Volume 25, Number 6, p.679-685, (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)
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)
Retinal Lesion Detection With Deep Learning Using Image Patches, Lam, C., Yu C., Huang L., and Rubin D. , Invest Ophthalmol Vis Sci, Jan 1, Volume 59, Number 1, p.590-596, (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)
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)
Differential Data Augmentation Techniques for Medical Imaging Classification Tasks, Hussain, Z., Gimenez F., Yi D., and Rubin D. , AMIA Annu Symp Proc, Volume 2017, p.979-984, (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)
Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment, Hernandez-Boussard, T., Kourdis P. D., Seto T., Ferrari M., Blayney D. W., Rubin D., and Brooks J. D. , AMIA Annu Symp Proc, Volume 2017, p.876-882, (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)
Software for Distributed Computation on Medical Databases: A Demonstration Project, Narasimhan, Balasubramanian, Rubin Daniel L., Gross Samuel M., Bendersky Marina, and Lavori Philip W. , Journal of Statistical Software, 2017-05-03, Volume 77, Number 13, p.22, (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
A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT, Cirujeda, P., Y. Cid Dicente, Muller H., Rubin D., Aguilera T. A., Loo B. W., Diehn M., Binefa X., and Depeursinge A. , IEEE Trans Med Imaging, Jul 18, (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)
A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound, Lekadir, K., Galimzianova A., Betriu A., Vila M. D., Igual L., Rubin D., Fernandez E., Radeva P., and Napel S. , IEEE J Biomed Health Inform, Nov 22, (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)
A method for normalizing pathology images to improve feature extraction for quantitative pathology, Tam, A., Barker J., and Rubin D. , Med Phys, Jan, Volume 43, Number 1, p.528, (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)
Quantitative Imaging in Cancer Clinical Trials, Yankeelov, T. E., Mankoff D. A., Schwartz L. H., Lieberman F. S., Buatti J. M., Mountz J. M., Erickson B. J., Fennessy F. M., Huang W., Kalpathy-Cramer J., et al. , Clin Cancer Res, Jan 15, Volume 22, Number 2, p.284-90, (2016)
Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study, 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, Aug 1, Volume 95, Number 5, p.1504-12, (2016)
Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data, Finlayson, S. G., Levy M., Reddy S., and Rubin D. L. , J Biomed Inform, Apr, Volume 60, p.104-13, (2016)
Using automatically extracted information from mammography reports for decision-support, Bozkurt, S., Gimenez F., Burnside E. S., Gulkesen K. H., and Rubin D. L. , J Biomed Inform, Aug, Volume 62, p.224-31, (2016)
2015
3D Markup of Radiological Images in ePAD, a Web-Based Image Annotation Tool, Moreira, D. A., Hage C., Luque E. F., Willrett D., and Rubin D. L. , Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on, 22-25 June 2015, p.97-102, (2015)
3D Riesz-wavelet based Covariance descriptors for texture classification of lung nodule tissue in CT, Cirujeda, P., Muller H., Rubin D., Aguilera T. A., Loo B. W., Diehn M., Binefa X., and Depeursinge A. , Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 25-29 Aug. 2015, p.7909-7912, (2015)
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients, Nicolasjilwan, M., Hu Y., Yan C., Meerzaman D., Holder C. A., Gutman D., Jain R., Colen R., Rubin D. L., Zinn P. O., et al. , J Neuroradiol, Jul, Volume 42, Number 4, p.212-21, (2015)
Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images, Chen, Q., de Sisternes L., Leng T., and Rubin D. L. , J Digit Imaging, Jun, Volume 28, Number 3, p.346-61, (2015)
Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography, Depeursinge, A., Chin A. S., Leung A. N., Terrone D., Bristow M., Rosen G., and Rubin D. L. , Invest Radiol, Apr, Volume 50, Number 4, p.261-267, (2015)
Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A Modular Approach with Ensemble of Convolutional Neural Networks, Ertosun, M. G., and Rubin D. L. , Proceedings of the American Medical Informatics Association, p.1899-1908, (2015)
Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection, Wu, Menglin, Leng Theodore, de Sisternes Luis, Rubin Daniel L., and Chen Qiang , Optics ExpressOptics Express, 2015/11/30, Volume 23, Number 24, p.31216-31229, (2015)
Automatic Classification of Cancer Tumors Using Image Annotations and Ontologies, Luque, E. F., Rubin D. L., and Moreira D. A. , Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on, 22-25 June 2015, p.368-369, (2015)
Content-based image retrieval in radiology: analysis of variability in human perception of similarity, Faruque, J., Beaulieu C. F., Rosenberg J., Rubin D. L., Yao D., and Napel S. , J Med Imaging (Bellingham), Apr, Volume 2, Number 2, p.025501, (2015)
Improved patch based automated liver lesion classification by separate analysis of the interior and boundary regions, Diamant, I., Hoogi A., Beaulieu C., Safdari M., Klang E., Amitai M., Greenspan H., and Rubin D. , IEEE J Biomed Health Inform, Sep 11, (2015)
Localization of damage in progressive hydroxychloroquine retinopathy on and off the drug: inner versus outer retina, parafovea versus peripheral fovea, de Sisternes, L., Hu J., Rubin D. L., and Marmor M. F. , Invest Ophthalmol Vis Sci, May, Volume 56, Number 5, p.3415-26, (2015)
Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities, Itakura, H., Achrol A. S., Mitchell L. A., Loya J. J., Liu T., Westbroek E. M., Feroze A. H., Rodriguez S., Echegaray S., Azad T. D., et al. , Sci Transl Med, Sep 2, Volume 7, Number 303, p.303ra138, (2015)
Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival, Wangaryattawanich, P., Hatami M., Wang J., Thomas G., Flanders A., Kirby J., Wintermark M., Huang E. S., Bakhtiari A. S., Luedi M. M., et al. , Neuro Oncol, Jul 22, (2015)
Ontology-based Image Navigation: Exploring 3.0-T MR Neurography of the Brachial Plexus Using AIM and RadLex, Wang, K. C., Salunkhe A. R., Morrison J. J., Lee P. P., Mejino J. L., Detwiler L. T., Brinkley J. F., Siegel E. L., Rubin D. L., and Carrino J. A. , Radiographics, Jan-Feb, Volume 35, Number 1, p.142-51, (2015)
Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: Classification of usual interstitial pneumonia, Depeursinge, A., Pad P., Chin A. S., Leung A. N., Rubin D. L., Muller H., and Unser M. , Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 16-19 April 2015, p.403-406, (2015)
Polychromatic X-Ray Absorptiometry to Quantify Breast Density Volume, Ratio and their Associated Breast Cancer Risk in Full-Digital Mammography, de Sisternes, L., Rothstein J. H., Jeffers A. M., Sieh W., and Rubin D. L. , Proceedings of the American Medical Informatics Association, p.28-29, (2015)
Probabilistic visual search for masses within mammography images using deep learning, Ertosun, M. G., and Rubin D. L. , 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 9-12 Nov. 2015, Washington, DC, p.1310-1315, (2015)
Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group, Shinagare, A. B., Vikram R., Jaffe C., Akin O., Kirby J., Huang E., Freymann J., Sainani N. I., Sadow C. A., Bathala T. K., et al. , Abdom Imaging, Mar 10, (2015)
Restricted Summed-Area Projection for Geographic Atrophy Visualization in SD-OCT Images, Chen, Q., Niu S., Shen H., Leng T., de Sisternes L., and Rubin D. L. , Transl Vis Sci Technol, Sep, Volume 4, Number 5, p.2, (2015)
Semantic Retrieval of Radiological Images with Relevance Feedback, Kurtz, Camille, Idoux Paul-André, Thangali Avinash, Cloppet Florence, Beaulieu Christopher F., and Rubin Daniel L. , Cham, p.11-25, (2015)
Visual Prognosis of Eyes Recovering From Macular Hole Surgery Through Automated Quantitative Analysis of Spectral-Domain Optical Coherence Tomography (SD-OCT) Scans, de Sisternes, L., Hu J., Rubin D. L., and Leng T. , Invest Ophthalmol Vis Sci, Jul, Volume 56, Number 8, p.4631-43, (2015)
Weighted locality-constrained linear coding for lesion classification in CT images, Yixuan, Yuan, Hoogi A., Beaulieu C. F., Meng M. Q. H., and Rubin D. L. , Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 25-29 Aug. 2015, p.6362-6365, (2015)
2014
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients, Nicolasjilwan, M., Hu Y., Yan C., Meerzaman D., Holder C. A., Gutman D., Jain R., Colen R., Rubin D. L., Zinn P. O., et al. , J NeuroradiolJ Neuroradiol, Jul 2, (2014)
Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography, Depeursinge, A., Chin A. S., Leung A. N., Terrone D., Bristow M., Rosen G., and Rubin D. L. , Invest Radiol, Dec 30, (2014)
Automated detection of ambiguity in BI-RADS assessment categories in mammography reports, Bozkurt, S., and Rubin D. , Stud Health Technol Inform, Volume 197, p.35-9, (2014)

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