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Titlebook: Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis; First MICCAI Worksho Luigi Manfredi,Seyed-Ahmad Ahmadi,Emanuele

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發(fā)表于 2025-3-21 18:51:40 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis
副標(biāo)題First MICCAI Worksho
編輯Luigi Manfredi,Seyed-Ahmad Ahmadi,Emanuele Trucco
視頻videohttp://file.papertrans.cn/462/461687/461687.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis; First MICCAI Worksho Luigi Manfredi,Seyed-Ahmad Ahmadi,Emanuele
描述This book constitutes the refereed proceedings of the first MICCAI Workshop, ISGIE 2022, Imaging Systems for GI Endoscopy, and the Fourth MICCAI Workshop, GRAIL 2022, GRaphs in biomedicAL Image and analysis, held in conjunction with MICCAI 2022, Singapore, September 18, 2022..ISGIE 2022 accepted 6 papers from the 8 submissions received.This workshop focuses on novel scientific contributions to vision systems, imaging algorithms as well as the autonomous system for endorobot for GI endoscopy. This includes lesion and lumen detection, as well as 3D reconstruction of the GI tract and hand-eye coordination..?GRAIL 2022 accepted 6 papers from the 10 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts..
出版日期Conference proceedings 2022
關(guān)鍵詞3d modelling; artificial intelligence; clustering algorithms; computer networks; computer systems; comput
版次1
doihttps://doi.org/10.1007/978-3-031-21083-9
isbn_softcover978-3-031-21082-2
isbn_ebook978-3-031-21083-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Real-Time Lumen Detection for?Autonomous Colonoscopyn metrics, while saving heavy labelling costs in terms of clinicians’ time. Our method for lumen detection runs at 60 ms per frame during the inference phase. Our experiments demonstrate the potential of our system in real-time environments, which contributes towards improving the automation of robo
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