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Publications

Found 327 results
2020
Accounting for data variability in multi-institutional distributed deep learning for medical imaging, Balachandar, Niranjan, Chang Ken, Kalpathy-Cramer Jayashree, and Rubin Daniel L. , Journal of the American Medical Informatics Association, Volume 27, Number 5, p.700-708, (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)
An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence, Sanyal, J., Banerjee I., Hahn L., and Rubin D. , AMIA Jt Summits Transl Sci Proc, Volume 2020, p.552-560, (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)
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives, Yi, Darvin, Grøvik Endre, Iv Michael, Tong Elizabeth, Zaharchuk Greg, and Rubin Daniel L. , (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)
Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI, Grovik, E., Yi D., Iv M., Tong E., Rubin D., and Zaharchuk G. , J Magn Reson Imaging, Jan, Volume 51, Number 1, p.175-182, (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)
Federated Learning for Breast Density Classification: A Real-World Implementation, Roth, Holger R., Chang Ken, Singh Praveer, Neumark Nir, Li Wenqi, Gupta Vikash, Gupta Sharut, Qu Liangqiong, Ihsani Alvin, Bizzo Bernardo C., et al. , Cham, p.181-191, (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)
Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers, Banerjee, I., de Sisternes L., Hallak J. A., Leng T., Osborne A., Rosenfeld P. J., Gregori G., Durbin M., and Rubin D. , Sci Rep, Sep 22, Volume 10, Number 1, p.15434, (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)
Weak supervision as an efficient approach for automated seizure detection in electroencephalography, Saab, K., Dunnmon J., Re C., Rubin D., and Lee-Messer C. , npj Digital MedicineNPJ Digit Med, Apr 20, Volume 3, Number 1, (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)
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, Jun, Volume 21, Number 3, p.591-598, (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 geographic atrophy segmentation for SD-OCT images based on two-stage learning model, Xu, R., Niu S., Chen Q., Ji Z., Rubin D., and Chen Y. , Comput Biol Med, Feb, Volume 105, p.102-111, (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)
Deep Active Lesion Segmentation, Hatamizadeh, A., Hoogi A., Sengupta D., Lu W. Y., Wilcox B., Rubin D., and Terzopoulos D. , Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Dlmia 2018, Volume 11861, p.98-105, (2019)
Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI, Grovik, E., Yi D., Iv M., Tong E., Rubin D., and Zaharchuk G. , J Magn Reson Imaging, May 2, (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)
Doubly Weak Supervision of Deep Learning Models for Head CT, Saab, K., Dunnmon J., Goldman R., Ratner A., Sagreiya H., Re C., and Rubin D. , Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Dlmia 2018, Volume 11766, p.811-819, (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 lung graph model for the classification of interstitial lung diseases on CT images, Vanoost, Guillaume, Cid Yashin Dicente, Rubin Daniel, and Depeursinge Adrien , Volume 10950, (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)
Prediction of Imaging Outcomes from Electronic Health Records: Pulmonary Embolism Case-Study, Banerjee, Imon, Sofela Miji, Amrhein Timothy, Rubin Daniel, Zamanian Roham, and Lungren Matthew , AMIA Annual Symposium, (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)
Putting the data before the algorithm in big data addressing personalized healthcare, Cahan, Eli M., Hernandez-Boussard Tina, Thadaney-Israni Sonoo, and Rubin Daniel L. , npj Digital Medicine, 2019/08/19, Volume 2, Number 1, p.78, (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)
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)
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)
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)
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)
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)

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