OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging (häftad)
Fler böcker inom
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
114
Utgivningsdatum
2019-10-11
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Hashimoto, Daniel (red.)/Habes, Mohamad (red.)/Loefstedt, Tommy (red.)/Ritter, Kerstin (red.)/Wang, Hongzhi (red.)
Illustrationer
33 Illustrations, color; 2 Illustrations, black and white; XVI, 114 p. 35 illus., 33 illus. in color
Dimensioner
234 x 156 x 7 mm
Vikt
195 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030326944
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging (häftad)

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

Häftad Engelska, 2019-10-11
683
  • Skickas inom 7-10 vardagar.
  • Gratis frakt inom Sverige över 199 kr för privatpersoner.
Kan levereras innan julafton!
Finns även som
Visa alla 1 format & utgåvor
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.
Visa hela texten

Passar bra ihop

  1. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging
  2. +
  3. Clean Code: A Handbook Of Agile Software Craftsmanship

De som köpt den här boken har ofta också köpt Clean Code: A Handbook Of Agile Software Crafts... av Robert C Martin (häftad).

Köp båda 2 för 1038 kr

Kundrecensioner

Har du läst boken? Sätt ditt betyg »

Innehållsförteckning

Proceedings of the Second International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019).- Feature Aggregation Decoder for Segmenting Laparoscopic Scenes.- Preoperative Planning for Guidewires employing Shape-Regularized Segmentation and Optimized Trajectories.- Guided unsupervised desmoking of laparoscopic images using Cycle-Desmoke.- Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration.- Live monitoring of hemodynamic changes with multispectral image analysis.- Towards a Cyber-Physical Systems Based Operating Room of the Future.- Proceedings of the Second 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.- Knowledge distillation for semi-supervised domain adaptation.- Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors.- Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation.- A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study.- Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites.