Institute of Informatics (II)
Topic: MedGIFT
Publications for topic "MedGIFT" sorted by first author
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, , and , Result Diversification in Social Image Retrieval: A Benchmarking Framework (2016), in: Multimedia Tools and Applications, 75:2(1301-1331)
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| , , , , and , Div400: A Social Image Retrieval Result Diversification Dataset, in: ACM Multimedia Systems, Singapore, 2014 |
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| , , , , , , , and , From Genes to Personalized Healthcare: Grid Solutions for the Life Sciencens, IOS Press, Studies in Health Technology and Informatics, volume 126, 2007 |
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| , , , , , , , , and , Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021 |
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, , , , , , , , and , The discriminative power and stability of radiomics features with CT variations: Task-based analysis in an anthropomorphic 3D-printed CT phantom (2021), in: Investigative Radiology, 56:12(820-825)
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| , , , , , , and , Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, in: SPIE Medical Imaging, pages 101400O-101400O-9, 2017 |
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| , , , , , , , , and , Assessment of the stability and discriminative power of radiomics features in liver lesions using an anthropomorphic 3D-printed CT phantom, in: Scientific session SGR-SSR, 2021 |
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| , , and , RadLex Terms and Local Texture Features for Multimodal Medical Case Retrieval, in: Multimodal Retrieval in the Medical Domain (MRMD) 2015, Vienna, Austria, Springer, 2015 |
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| , and , Combining Radiology Images and Meta-data for Multimodal Medical Case-Based Retrieval, Springer, 2017 |
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| , , and , Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3, in: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI, pages 22-26, CEUR-WS, 2015 |
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| , , and , Texture classification of anatomical structures in CT using a context-free machine learning approach, in: SPIE Medical Imaging 2015, pages 94140W-94140W-14, SPIE, 2015 |
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| , , , and , Overview of the VISCERAL Retrieval Benchmark 2015, in: Multimodal Retrieval in the Medical Domain, Vienna, Austria, Springer, 2015 |
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| , , , and , Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, pages 353-360, Springer Berlin Heidelberg, 2013 |
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| , , , , , , , , , , , , , , , and , VISCERAL - VISual Concept Extraction Challenge in RAdioLogy: ISBI 2014 Challenge Organization, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, pages 6-15, 2014 |
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| and , Hierarchical Multi-atlas Based Segmentation for Anatomical Structures: Evaluation in the VISCERAL Anatomy Benchmarks, in: Medical Computer Vision. Large Data in Medical Imaging, Springer, 2014 |
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| and , Multi atlas–based segmentation with data driven refinement, in: Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, Valencia, Spain, pages 605-608, IEEE, 2014 |
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| and , Hierarchical multi-structure segmentation guided by anatomical correlations, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, pages 32-36, 2014 |
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| , , , and , Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, chapter Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, Springer, 2017 |
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and , Multi-Structure Atlas-Based Segmentation Using Anatomical Regions Of Interest, in: Medical Computer Vision. Large Data in Medical Imaging, Nagoya, Japan, pages 217-221, Springer International Publishing, 2014
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| , , , , , , and , Elsevier book on Texture Analysis, chapter Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
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, , , , , , , , , , , , , , , , , , , , , , , , , , and , Cloud–based Evaluation of Organ Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks (2016), in: IEEE Transactions on Medical Imaging
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, , , , , and , A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT (2020), in: Computers in Biology and Medicine(103962)
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| , , , , , , , and , Are species identification tools biodiversity-friendly, in: ACM Multimedia Workshop on Multimedia Analysis of Ecological Data, Barcelona, Spain, 2014 |
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| , , , , , , , and , Are multimedia identification tools biodiversity-friendly, in: ACM Multimedia Workshop on Multimedia Analysis of Ecological Data, Barcelona, Spain, 2013 |
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| , , , , and , Implicit biodiversity monitoring from mobile search logs (2016), in: ACM Multimedia |
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| , , , , , , , , and , LifeCLEF 2016: Multimedia Life Species Identication Challenges, in: CLEF Proceedings, Springer, 2016 |
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, , , , , , , , and , LifeCLEF 2015: Multimedia Life Species Identification Challenges, in: CLEF 2015 Proceedings, 2015
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| , , , , , , , and , LifeCLEF 2014: multimedia life species identification challenges, in: Proceedings of CLEF 2014, CEUR-WS, 2014 |
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| , , , , , , , , and , LifeCLEF: Multimedia Life Species Identification, in: Workshop on Environmental Multimedia Retrieval at ACM MM, Orlando, FL, USA, 2014 |
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, and , Using Probability Maps for Multi-organ Automatic Segmentation, in: MICCAI MCV workshop, Nagoya, Japan, 2014
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| and , Locating seed points for multi-organ automatic segmentation using non-rigid registration and organ annotations, in: SPIE Medical Imaging, 2015 |
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| and , Finding seed points for organ segmentation using example annotations, in: SPIE Medical Imaging 2014, San Diego, CA, USA, 2014 |
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| , and , Rotation-Covariant Tissue Analysis for Interstitial Lung Diseases Using Learned Steerable Filters: Performance Evaluation and Relevance for Diagnostic Aid (2018), in: Computerized Medical Imaging and Graphics, 64(1-11) |
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, , and , Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)
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| , and , Applying Machine Learning to Gait Analysis Data for Disease Identification, in: Medical Informatics Europe (MIE), Madrid, Spain, 2015 |
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| , and , A Demo of Multimodal Medical Retrieval, in: Workshop on Content-based Multimedia Indexing (CBMI), Bucharest, Romania, 2016 |
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| , , and , Image Retrieval in Medicine: The imageCLEF 2009 Challenge, in: RSNA 2009, Chicago, IL, USA, 2009 |
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| , , , , , and , Retrieving Similar Cases from the Medical Literature - The ImageCLEF experience, in: Medinfo 2010, Cape Town, South Africa, pages 1189-1193, IOS Press, 2010 |
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| , , , , and , Web-based medical image retrieval systems: a demonstration of the utility of text, images, and user input in retrieving relevant images, Demonstration at Medinfo 2010, 2010 |
, , , , and , Evaluating performance of biomedical image retrieval systems - an overview of the medical image retrieval task at ImageCLEF 2004-2013 (2015), in: Computerized Medical Imaging and Graphics, 39:55-61
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| , , and , Query Analysis to Improve medical Image Retrieval, in: Society for Imaging Informatics in Medicine (SIIM) 2008, 2008 |
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| and , Evaluation campaigns in medical imaging, in: Biomedical Image Processing - Methods and Applications, pages 497-520, Springer, 2011 |
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| , , , , and , Overview of the CLEF 2011 medical image classification and retrieval tasks, in: CLEF 2011 working notes, Amsterdam, The Netherlands, 2011 |
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, , and , Special Issue (2015), in: Computerized Medical Imaging and Graphics, 39
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| , , , , , , and , Khresmoi Professional: Multilingual, Multimodal Professional Medical Search, in: European Conference on Information Retrieval, Amsterdam, Netherlands, 2014, 2014 |
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| , and , Safe Storage and Multi-modal Search for Medical Images, in: MIE 2011, Oslo, Norway, 2011 |
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| and , Semantics in Radiology, in: ECIR EPOS poster, Vienna, Austria, 2015 |
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| and , Semantics in radiology, in: ECR, Vienna, Austria, 2013 |
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, , , , , , and , Creating a Large-Scale Silver Corpus from Multiple Algorithmic Segmentations, in: MICCAI medical computer vision workshop at MICCAI 2015, Munich, Germany, 2016
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| , , , and , A semantic framework for the retrieval of similar radiological images based on medical annotations, in: IEEE International Conference on Image Processing, Paris, France, pages 2241-2245, IEEE, 2014 |
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| 1-50 | 51-100 | 101-150 | 151-200 | 201-250 | 251-300 | 301-350 | 351-400 | 401-450 | 451-500 | 501-550 | 551-590 |