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000915849 019__ $$a1125993751
000915849 020__ $$a9783030326951$$q(electronic book)
000915849 020__ $$a3030326950$$q(electronic book)
000915849 020__ $$z9783030326944
000915849 0247_ $$a10.1007/978-3-030-32695-1$$2doi
000915849 0247_ $$a10.1007/978-3-030-32
000915849 035__ $$aSP(OCoLC)on1123191334
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000915849 050_4 $$aRD29.7
000915849 08204 $$a617.00285$$223
000915849 1112_ $$aOR 2.0 (Workshop)$$n(2nd :$$d2019 :$$cShenzhen Shi, China)
000915849 24510 $$aOR 2.0 context-aware operating theaters and machine learning in clinical Neuroimaging :$$bsecond International Workshop, OR 2.0 2019, and second International Workshop, MLCN 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings /$$cLuping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang (eds.).n
000915849 2463_ $$aOR 2.0 2019
000915849 2463_ $$aMLCN 2019
000915849 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000915849 300__ $$a1 online resource (xvi, 114 pages) :$$billustrations.
000915849 336__ $$atext$$btxt$$2rdacontent
000915849 337__ $$acomputer$$bc$$2rdamedia
000915849 338__ $$aonline resource$$bcr$$2rdacarrier
000915849 4901_ $$aLecture notes in computer science ;$$v11796
000915849 4901_ $$aLNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
000915849 500__ $$aInternational conference proceedings.
000915849 500__ $$aIncludes author index.
000915849 5050_ $$aIntro; Additional Workshop Editors; OR 2.0 2019 Preface; OR 2.0 2019 Organization; MLCN 2019 Preface; MLCN 2019 Organization; Contents; Proceedings of the 2nd International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019); Feature Aggregation Decoder for Segmenting Laparoscopic Scenes; 1 Introduction; 2 Method; 2.1 Xception Encoder; 2.2 Feature Aggregation Decoder; 3 Experimental Results and Discussions; 4 Conclusions; References; Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories; 1 Introduction; 2 Materials and Methods
000915849 5058_ $$a3 Experiments4 Results; 5 Discussion and Conclusion; References; Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke; 1 Introduction; 2 Method; 2.1 Guided-Unsharp Upsample Loss; 2.2 Aggregate Loss Function; 2.3 Atrous Convolution Feature Extraction Module; 2.4 Generator and Discriminator Networks; 3 Experimentation and Results; 3.1 Dataset and Implementation Details; 3.2 Results; 4 Conclusion; References; Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration; 1 Introduction; 2 Methods; 2.1 RSD Model
000915849 5058_ $$a2.2 Unsupervised Temporal Video Segmentation Model2.3 Combined Learning Pipelines; 2.4 Corridor-Based RSD Loss Function; 3 Evaluation; 3.1 Baselines; 3.2 Results; 4 Conclusion; References; Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis; 1 Introduction; 2 Methods; 2.1 Multispectral Imaging Hardware; 2.2 End-to-End Deep Learning Pipeline for Multispectral Image Analysis; 3 Experiments and Results; 3.1 In Silico Quantitative Validation; 3.2 In Vivo Qualitative Validation; 4 Discussion; References; Towards a Cyber-Physical Systems Based Operating Room of the Future
000915849 5058_ $$a1 Introduction2 Methods; 2.1 Intelligent Surgical Theatre Architecture; 2.2 Cyber-Twin; 2.3 Cognitive Engine and Machine Learning; 3 RadioFrequency Ablation Needle Insertion Robot; 4 Discussion and Conclusion; References; Proceedings of the 2nd International Workshop on Machine Learning in Clinical Neuroimaging: Entering the Era of Big Data via Transfer Learning and Data Harmonization (MLCN 2019); Deep Transfer Learning for Whole-Brain FMRI Analyses; 1 Introduction; 2 Methods; 2.1 Data; 2.2 DeepLight; 3 Results; 3.1 Pre-training Data; 3.2 Test Data; 4 Conclusion; References
000915849 5058_ $$aKnowledge Distillation for Semi-supervised Domain Adaptation1 Introduction; 2 Related Work; 3 Methods; 3.1 Knowledge Distillation for Domain Adaptation; 4 Experiments and Results; 4.1 Databases; 4.2 Experimental Setup; 4.3 Results; 5 Discussion; References; Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors; 1 Introduction; 2 Data; 2.1 Data Pre-processing and Feature Extraction; 3 The Relevance Vector Machine for Data Harmonization; 4 Results; 4.1 Verification of Observable Correlations in Data; 4.2 Harmonization of Healthy Population Data Based on RVM
000915849 506__ $$aAccess limited to authorized users.
000915849 520__ $$aThis 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.
000915849 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 16, 2019).
000915849 650_0 $$aComputer-assisted surgery$$vCongresses.
000915849 650_0 $$aSurgical robots$$vCongresses.
000915849 650_0 $$aDiagnostic imaging$$xDigital techniques$$vCongresses.
000915849 7001_ $$aZhou, Luping,$$eeditor.
000915849 7001_ $$aSarikaya, Duygu,$$eeditor.
000915849 7001_ $$aKia, Seyed Mostafa,$$eeditor.
000915849 7001_ $$aSpeidel, Stefanie,$$eeditor.
000915849 7001_ $$aMalpani, Anand,$$eeditor.
000915849 7001_ $$aHashimoto, Daniel,$$eeditor.
000915849 7001_ $$aHabes, Mohamad,$$eeditor.
000915849 7001_ $$aLöfstedt, Tommy,$$eeditor.
000915849 7001_ $$aRitter, Kerstin,$$eeditor.
000915849 7001_ $$aWang, Hongzhi,$$eeditor.
000915849 7112_ $$aMLCN (Workshop)$$n(2nd :$$d2019 :$$cShenzhen Shi, China),$$jjointly held conference.
000915849 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(22nd :$$d2019 :$$cShenzhen Shi, China),$$jjointly held conference.
000915849 830_0 $$aLecture notes in computer science ;$$v11796.
000915849 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000915849 852__ $$bebk
000915849 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-32695-1$$zOnline Access$$91397441.1
000915849 909CO $$ooai:library.usi.edu:915849$$pGLOBAL_SET
000915849 980__ $$aEBOOK
000915849 980__ $$aBIB
000915849 982__ $$aEbook
000915849 983__ $$aOnline
000915849 994__ $$a92$$bISE