Or 20 Context Aware Operating Theaters And Machine Learning In Clinical Neuroimaging

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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging
Author :
Publisher : Springer Nature
Total Pages : 126
Release :
ISBN-10 : 9783030326951
ISBN-13 : 3030326950
Rating : 4/5 (950 Downloads)

Book Synopsis OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging by : Luping Zhou

Download or read book OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging written by Luping Zhou and published by Springer Nature. This book was released on 2019-10-10 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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