Daniel L. Rubin - HES SO Valais Publications - Aigaion 2.0

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Primeiro nome(s):	Daniel L.
Sobrenome(s):	Rubin

Guillaume Vanoost, Yashin Dicente Cid, Daniel L. Rubin and Adrien Depeursinge, A lung graph model for the classification of interstitial lung disease on CT images, in: SPIE Medical Imaging 2019: Computer-Aided Diagnosis, International Society for Optics and Photonics, páginas 869-876, SPIE, 2019

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Pierre Starkov, Todd A. Aguilera, Daniel Golden, David B. Shultz, Nicholas Trakul, Peter G. Maxim, Quynh-Thu Le, Billy W. Loo Jr., Maximilian Diehn, Adrien Depeursinge and Daniel L. Rubin, The Use of Texture Based Radiomics CT Analysis to Predict Outcomes in Early-Stage Non-Small Cell Lung Cancer Treated with Stereotactic Ablative Radiotherapy (2018), in: The British Journal of Radiology, 92:1094(20180228)

[]: IF 2016 = 2.05

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Roger Schaer, Yashin Dicente Cid, Emel Alkim, Sheryl John, Daniel L. Rubin and Adrien Depeursinge, Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology: Intensity and Texture Analysis on the ePAD Platform, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, páginas 379-410, Elsevier, 2017

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Pol Cirujeda, Yashin Dicente Cid, Henning Müller, Daniel L. Rubin, Todd A. Aguilera, Billy W. Loo Jr., Maximilian Diehn, Xavier Binefa and Adrien Depeursinge, A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT (2016), in: IEEE Transactions on Medical Imaging, 35:12(2620-2630)

[]: IF 2015 = 3.76
[]: video presentation of the paper: https://portal.klewel.com/watch/webcast/valaiswallis-ai-workshop-reproducibility-in-research/talk/9

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Adrien Depeursinge, Tunde Szilágyi, Yan Liu, Kazmer Kovacs, Reena Thomas, Kristen Yeom, Nancy Fischbein, Daniel L. Rubin, Michael Iv and Olivier Gevaert, Locoregional radiogenomic models to capture gene expression heterogeneity in glioblastoma (2018), in: biorXiv

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Geoffrey Sonn, Lewis D. Hahn, Richard Fan, Jocelyn Barker, Adrien Depeursinge and Daniel L. Rubin, Quantitative Image Texture Analysis Predicts Malignancy on Multiparametric Prostate MRI, in: 91st Annual Meeting of the Western Section of American Urological Association, Indian Wells, CA, USA, 2015

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Jocelyn Barker, Assaf Hoogi, Adrien Depeursinge and Daniel L. Rubin, Automated Classification of Brain Tumor Type in Whole-Slide Digital Pathology Images Using Local Representative Tiles (2016), in: Medical Image Analysis, 30(60-71)

[]: IF 2014 = 3.654

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Maria De-Arteaga, Ivan Eggel, Bao H. Do, Daniel L. Rubin, Charles E. Kahn Jr. and Henning Müller, Comparing Image Search Behaviour in the ARRS GoldMiner Search Engine and a Clinical PACS/RIS (2015), in: Journal of Biomedical Informatics, 56(57-64)

[]: IF 2013=2.482

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Pol Cirujeda, Henning Müller, Daniel L. Rubin, Todd A. Aguilera, Billy W. Loo Jr., Maximilian Diehn, Xavier Binefa and Adrien Depeursinge, 3D Riesz–wavelet based Covariance descriptors for texture classification of lung nodule tissue in CT, in: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), páginas 7909-7912, 2015

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Imon Banerjee, Sadhika Malladi, Daniela Lee, Adrien Depeursinge, Melinda Telli, Jafi Lipson, Daniel Golden and Daniel L. Rubin, Assessing Treatment Response in Triple Negative Breast Cancer from Quantitative Image Analysis in Perfusion MRI (2017), in: Journal of Medical Imaging, 5:1(5-10)

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Adrien Depeursinge, Pedram Pad, Anne C. Chin, Ann N. Leung, Daniel L. Rubin, Henning Müller and Michael Unser, Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: classification of usual interstitial pneumonia, in: IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, USA, páginas 403-406, IEEE, 2015

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Adrien Depeursinge, Anne C. Chin, Ann N. Leung, Donato Terrone, Michael Bristow, Glenn Rosen and Daniel L. Rubin, Automated Classification of Usual Interstitial Pneumonia using Regional Volumetric Texture Analysis in High-Resolution CT (2015), in: Investigative Radiology, 50:4(261-267)

[]: Impact Factor=4.453

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Adrien Depeursinge, Masahiro Yanagawa, Ann N. Leung and Daniel L. Rubin, Predicting Adenocarcinoma Recurrence Using Computational Texture Models of Nodule Components in Lung CT (2015), in: Medical Physics, 42:4(2054-2063)

[]: Impact Factor=3.012

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Camille Kurtz, Adrien Depeursinge, Sandy Napel, Christopher F. Beaulieu and Daniel L. Rubin, On combining visual and ontological similarities for medical image retrieval applications (2014), in: Medical Image Analysis, 18:7(1082–1100)

[]: Impact Factor=4.087

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Sadhika Malladi, Daniela Lee, Adrien Depeursinge and Daniel L. Rubin, Predicting Treatment Response in Triple Negative Breast Cancer Through Quantitative Image Analysis in Perfusion MRI, in: 6th Annual Symposium of the Center for Biomedical Imaging at Stanford, Stanford, CA, USA, 2014

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Jocelyn Barker, Adrien Depeursinge, Robert West and Daniel L. Rubin, Applications of Texture in Digital Pathology, in: 6th Annual Symposium of the Center for Biomedical Imaging at Stanford, Stanford, CA, USA, 2014

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Camille Kurtz, Adrien Depeursinge, Sandy Napel, Christopher F. Beaulieu and Daniel L. Rubin, A semantic framework for the retrieval of similar radiological images based on medical annotations, in: IEEE International Conference on Image Processing, Paris, France, páginas 2241-2245, IEEE, 2014

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Adrien Depeursinge, Anne C. Chin, Ann N. Leung, Glenn Rosen and Daniel L. Rubin, A computerized score for the automated differentiation of usual interstitial pneumonia from regional volumetric texture analysis, in: Radiological Society of North America, scientific paper, 2014

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Adrien Depeursinge, Camille Kurtz, Christopher F. Beaulieu, Sandy Napel and Daniel L. Rubin, Texture-Based Computational Models of Biomedical Tissue in Radiological Images: Unveiling the Invisible, in: 14th Annual Symposium on Biomedical Computation at Stanford, Stanford, CA, USA, 2014

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Adrien Depeursinge, Camille Kurtz, Christopher F. Beaulieu, Sandy Napel and Daniel L. Rubin, Predicting Visual Semantic Descriptive Terms from Radiological Image Data: Preliminary Results with Liver Lesions in CT (2014), in: IEEE Transactions on Medical Imaging, 33:8(1-8)

[]: Impact Factor=4.027

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