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Titlebook: Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data ; 6th Joint Internatio M. Jorge Cardoso

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發(fā)表于 2025-3-21 18:07:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data
副標(biāo)題6th Joint Internatio
編輯M. Jorge Cardoso,Tal Arbel,Gustavo Carneiro
視頻videohttp://file.papertrans.cn/474/473239/473239.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data ; 6th Joint Internatio M. Jorge Cardoso
描述.This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017...The 6 full papers presented at CVII-STENT 2017 and the 11 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing..
出版日期Conference proceedings 2017
關(guān)鍵詞artificial intelligence; classification; classifiers; computer aided design; computer vision; computerize
版次1
doihttps://doi.org/10.1007/978-3-319-67534-3
isbn_softcover978-3-319-67533-6
isbn_ebook978-3-319-67534-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Networkally analyzed using the trained CNN by considering the intensity values of neighboring voxels that belong to its patch. Experiments were performed with TOF MRA images of five healthy subjects, using varying numbers of images to train the CNN. Cross validations revealed that the proposed framework is
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發(fā)表于 2025-3-22 01:59:45 | 只看該作者
VOIDD: Automatic Vessel-of-Intervention Dynamic Detection in PCI Procedurestion and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vessel-of-intervention with accuracy in the range of?. or above and absence of tip with accuracy in range of?. or above depending on the test cas
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Towards an Efficient Way of Building Annotated Medical Image Collections for Big Data Studies assigning tasks such as labeling and contouring for big data medical imaging studies. This is a web-based platform and provides the tooling for both researchers and annotators, all within a simple dynamic user interface. Our annotation platform also streamlines the process of iteratively training a
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Yaniv Gur,Mehdi Moradi,Hakan Bulu,Yufan Guo,Colin Compas,Tanveer Syeda-Mahmoodengineering tools for improving pattern control and array efficiency including lattice selection, subarrray technology and pattern synthesis. Equations and figurers quantify the phenomena being described and provide the reader with the tools to tradeoff various performance features. The discussions
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