Schlagworte:
Publikationen von Yashin Dicente Cid sortiert nach Titel
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, , , und , 3-D Solid Texture Classification Using Locally-Oriented Wavelet Transforms (2017), in: IEEE Transactions on Image Processing, 26:4(1899-1910)
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, , , , , , , und , 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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| , , und , A 3D Scene Registration Method via Covariance Descriptors and an Evolutionary Stable Strategy Game Theory Solver (2015), in: International Journal of Computer Vision(1-24) |
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| , , und , A Graph Model of the Lungs with Morphology-based Structure for Tuberculosis Type Classification, in: The 26th International Conference on Information Processing in Medical Imaging (IPMI), 2019 |
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| , , und , 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, Seiten 869-876, SPIE, 2019 |
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, , , , , und , 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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| , , , , , und , A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT images, in: MICCAI Workshop on Medical Computer Vision: Algorithms for Big Data, Seiten 58-68, 2016 |
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| , , , , und , An Automatically Generated Texture-based Atlas of the Lungs, in: SPIE Medical Imaging, SPIE, 2018 |
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| , , , und , Analyzing the trade-off between training session time and performance in myoelectric hand gesture recognition during upper limb movement, in: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, Canada, 2019 |
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| , und , BatmanLab in the ImageCLEF Tuberculosis task 2017, in: ImageCLEF working Notes, Dublin, Ireland, 2017 |
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, , , , , , , , , , , , , , , , , , , , , , , , , , und , Cloud–based Evaluation of Organ Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks (2016), in: IEEE Transactions on Medical Imaging
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| , , , , und , Comparing 18-FDG PET 3D texture attributes for the prediction of survival and recurrence in oropharyngeal cancers treated with radiotherapy, in: Workshop on the Prediction and Modeling of response to Molecular and External Beam Radiotherapies, Le Bono, France, 2017 |
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| , , und , Efficient and fully automatic segmentation of the lungs in CT volumes, in: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI, New York, USA, Seiten 31-35, CEUR-WS, 2015 |
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| , , , und , Finding and Classifying Tuberculosis Types for a Targeted Treatment: MedGIFT--UPB Participation in the ImageCLEF 2017 tuberculosis Task, in: ImageCLEF working Notes 2017, Dublin, Ireland, 2017 |
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| , , , und , From Local to Global: A Holistic Lung Graph Model, in: MICCAI 2018, Granada, Spain, 2018 |
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| , , und , 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, Seiten 22-26, CEUR-WS, 2015 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature and Security Applications, in: CLEF conference Proceedings, Lugano, Switzerland, Springer LNCS, 2019 |
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| , , , , , , , , , , , , , , , , , , , , , , , , und , ImageCLEF 2019: Multimedia Retrieval in Medical Nature, Security and Lifelogging Applications, in: ECIR 2019, Cologne, Germany, 2019 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , und , ImageCLEF 2021: Multimedia Retrieval in Medical, Nature, Internet and Social Media, in: CLEF proceedings, Springer, 2021 |
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| , , , , , , , , , , , , , , , , , , , , , , und , ImageCLEF 2022: Multimedia Retrieval in Medical, Nature, Fusion, and Internet Applications, in: European Conference on Information Retrieval, Springer, Seiten 382--389, 2022 |
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| und , Lung Graph-Model Classification with SVM and CNN for Tuberculosis Severity Assessment and Automatic CT Report Generation, in: CLEF working notes 2019, Lugano Switzerland, 2019 |
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| , Lung Tissue Analysis: From Local Visual Descriptors To Global Modeling, University of Geneva, 2018 |
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| , , , , und , Making Sense of Large Data Sets without Annotations: Analyzing Age–related Correlations from Lung CT Scans, in: SPIE Medical Imaging, SPIE, 2017 |
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| , , und , MCOV: A Covariance Descriptor for Fusion of Texture and Shape Features in 3D Point Clouds (2014), in: 3D Vision (3DV), 2014 2nd International Conference on, 1(551-558) |
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| , , , , , , , , und , Nouveaux paramètres métaboliques du FDG-PET/TDM pour prédire la récurrence et la survie des cancers du col utérin traité par radio-chimiothérapie, in: Société Française de radiothérapie Oncologique, 2017 |
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| , , und , Overview of ImageCLEF 2017 Tuberculosis Task – Predicting Tuberculosis Type and Drug Resistances, in: CLEF 2017 working Notes, Dublin Ireland, CEUR, 2017 |
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, , , , , , , , , , , , , , , und , Overview of ImageCLEF 2017: Information extraction from images, in: CLEF Proceedings, 2017
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| , , , , , , , , , , , , , , , , , , und , Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation, in: CLEF conference proceeding, Avignon, France, Springer, 2018 |
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| , , und , Overview of ImageCLEFtuberculosis 2018 - Detecting multi-drug resistance, classifying tuberculosis type, and assessing severity score, in: CLEF working notes, CEUR, 2018 |
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| , , , , und , Overview of ImageCLEFtuberculosis 2019 --- Automatic CT--based Report Generation and Tuberculosis Severity Assessment, in: CLEF working notes, Lugano, Switzerland, 2019 |
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| , , , , und , Overview of ImageCLEFtuberculosis 2020: Automatic CT-based report generation, in: CLEF working notes, CEUR, 2020 |
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| , , , und , Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification, in: CLEF working notes, 2021 |
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| , , und , Overview of ImageCLEFtuberculosis 2022 -- CT-based Caverns Detection and Report, in: CLEF working Notes, 2022 |
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| , , und , Overview of ImageCLEFtuberculosis 2022: CT-based cavern detection and report, in: Proceedings of CLEF (Conference and Labs of the Evaluation Forum) 2022 Working Notes, 2022 |
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical Lifelogging, Nature and Internet Applications, in: CLEF Proceedings, 2020 |
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| , , , , , , , , , , , , , , , , , , , , , und , Overview of the ImageCLEF 2022: Multimedia Retrieval in Medical, Social Media and Nature Applications, in Experimental IR Meets Multilinguality, Multimodality, and Interaction, in: CLEF 2022 Proceedings, Springer, 2022 |
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, , , , , , , , , , , , und , PET-based predictive survival model after radiotherapy for head and neck cancer (2019), in: European Journal of Nuclear Medicine and Molecular Imaging, 46:3(638-649)
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| , , , , und , Pulmonary Embolism Detection using Localized Vessel-Based Features in Dual Energy CT, in: SPIE Medical Imaging, Seiten 941407-941407-10, SPIE, 2015 |
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| , und , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, Demonstration at SPIE MI 2017, 2017 |
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| , , , , , und , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, Seiten 349-377, Elsevier, 2017 |
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| , , und , 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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| und , Texture-based Graph Model of the Lungs for Drug Resistance Detection, Tuberculosis Type Classification, and Severity Scoring: Participation in ImageCLEF 2018 Tuberculosis Task, in: CLEF working notes, Avignon, France, CEUR, 2018 |
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| , und , Textured Graph--based Model of the Lungs: Application on Tuberculosis Type Classification and Multi--Drug Resistance Prediction, in: CLEF 2018, Avignon, France, 2018 |
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| , und , Textured Graph-model of the Lungs for Tuberculosis Type Classification and Drug Resistance Prediction: participation in ImageCLEF 2017, in: ImageCLEF working Notes 2017, Dublin, Ireland, 2017 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications, in: European Conference on Information Retrieval, Springer, 2021 |
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| , , , , und , Training a deep neural network for small and highly heterogeneous MRID datasets for cancer grading, in: EMBC Conference, IEEE, 2020 |
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| , , , , , , , , und , Valeur de la TEP au 18-FDG pour prédire la récidive dans les cancers ORL non oropharyngé traités par radio-chimiothérapie, in: Société Française de radiothérapie Oncologique, 2017 |
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| , , , , und , 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, Seiten 379-410, Elsevier, 2017 |
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