株式会社極東書店トップ ＞ 商品一覧 ＞ OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging: Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. 2019 ed.
商品詳細
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging: Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. 2019 ed.
・ISBN 978-3-030-32694-4 paper EUR 49.99
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| 著者・編者 | Zhou, Luping / Sarikaya, Duygu / Kia, Seyed Mostafa / Speidel, Stefanie / Malpani, Anand / Hashimoto, Daniel / Habes, Mohamad / Loefstedt, Tommy / Ritter, Kerstin / Wang, Hongzhi (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2019 |
| ページ数 | 114 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A01-86944＞ |
解説
This book constitutes the refereed proceedings of the Second International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the Second International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.
For OR 2.0 all 6 submissions were accepted for publication. They aim to highlight the potential use of machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors, wearable and implantable electronics and robots, visual attention models, cognitive models, decision support networks to enhance surgical procedural assistance, context-awareness and team communication in the operating theater, human-robot collaborative systems, and surgical training and assessment.
MLCN 2019 accepted 6 papers out of 7 submissions for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.
Chapter 5 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.