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Publications

Found 28 results
Filters: Author is Rubin, D.  [Clear All Filters]
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)
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)
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)
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
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 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)
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)
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)
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)
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)
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)
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)
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)
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)
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, Dec, Volume 35, Number 12, p.2620-2630, (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)
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)
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)
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)
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)
Extracting imaging observation entities in mammography reports, Bozkurt, S., and Rubin D. , Stud Health Technol Inform, Volume 205, p.1223, (2014)
Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT, Depeursinge, A., Kurtz C., Beaulieu C., Napel S., and Rubin D. , IEEE Trans Med ImagingIEEE Trans Med Imaging, Aug, Volume 33, Number 8, p.1669-76, (2014)
2013
Annotation for information extraction from mammography reports, Bozkurt, S., Gulkesen K. H., and Rubin D. , Stud Health Technol Inform, Volume 190, p.183-5, (2013)
2012
Automatic annotation of radiological observations in liver CT images, Gimenez, F., Xu J., Liu Y., Liu T., Beaulieu C., Rubin D., and Napel S. , AMIA Annu Symp Proc, Volume 2012, p.257-63, (2012)
Automatic classification of mammography reports by BI-RADS breast tissue composition class, Percha, B., Nassif H., Lipson J., Burnside E., and Rubin D. , J Am Med Inform Assoc, Sep-Oct, Volume 19, Number 5, p.913-6, (2012)
Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval, Xu, J., Napel S., Greenspan H., Beaulieu C. F., Agrawal N., and Rubin D. , Med PhysMed Phys, Sep, Volume 39, Number 9, p.5405-18, (2012)
2009
Comparison of concept recognizers for building the Open Biomedical Annotator, Shah, N. H., Bhatia N., Jonquet C., Rubin D., Chiang A. P., and Musen M. A. , BMC Bioinformatics, Volume 10 Suppl 9, p.S14, (2009)