OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging (eBook, PDF)

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
Redaktion: Zhou, Luping; Ritter, Kerstin; Löfstedt, Tommy; Habes, Mohamad; Hashimoto, Daniel; Malpani, Anand; Speidel, Stefanie; Kia, Seyed Mostafa; Sarikaya, Duygu; Wang, Hongzhi

• Format: PDF

Produktbeschreibung

Chapter 5 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 114
• Erscheinungstermin: 10. Oktober 2019
• Englisch
• ISBN-13: 9783030326951
• Artikelnr.: 57903023

Inhaltsangabe

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.