Trefwoorden:
Publicaties van Daniel L. Rubin gesorteerd op tijdschrift en type
biorXiv
| , , , , , , , , en , Locoregional radiogenomic models to capture gene expression heterogeneity in glioblastoma (2018), in: biorXiv |
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IEEE Transactions on Medical Imaging
, , , , , , , en , 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)
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, , , en , 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)
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Investigative Radiology
, , , , , en , Automated Classification of Usual Interstitial Pneumonia using Regional Volumetric Texture Analysis in High-Resolution CT (2015), in: Investigative Radiology, 50:4(261-267)
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Journal of Biomedical Informatics
, , , , en , Comparing Image Search Behaviour in the ARRS GoldMiner Search Engine and a Clinical PACS/RIS (2015), in: Journal of Biomedical Informatics, 56(57-64)
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Journal of Medical Imaging
| , , , , , , en , 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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Medical Image Analysis
, , en , Automated Classification of Brain Tumor Type in Whole-Slide Digital Pathology Images Using Local Representative Tiles (2016), in: Medical Image Analysis, 30(60-71)
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, , , en , On combining visual and ontological similarities for medical image retrieval applications (2014), in: Medical Image Analysis, 18:7(1082–1100)
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Medical Physics
, , en , Predicting Adenocarcinoma Recurrence Using Computational Texture Models of Nodule Components in Lung CT (2015), in: Medical Physics, 42:4(2054-2063)
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The British Journal of Radiology
, , , , , , , , , en , 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)
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Alle publicaties van type Incollection
2017
| , , , , en , 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, pagina's 379-410, Elsevier, 2017 |
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Alle publicaties van type Inproceedings
2019
| , , en , 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, pagina's 869-876, SPIE, 2019 |
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2015
| , , , , , , en , 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), pagina's 7909-7912, 2015 |
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| , , , , , en , 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, pagina's 403-406, IEEE, 2015 |
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| , , , , en , 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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2014
| , , , en , 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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| , , , en , A semantic framework for the retrieval of similar radiological images based on medical annotations, in: IEEE International Conference on Image Processing, Paris, France, pagina's 2241-2245, IEEE, 2014 |
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| , , en , 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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| , , en , 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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| , , , en , 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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