Intravascular Imaging and Computer Assisted Stenting and Large Scale Annotation of Biomedical Data and Expert Label Synthesis Danail 3319675346 9783319675343

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Product details:

• ISBN 10: 3319675346
• ISBN 13: 9783319675343
• Author: Danail Stoyanov

This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 6 full papers presented at CVII-STENT 2017 and the 11 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.

Table of contents:

1. 6th Joint International Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017
Robust Detection of Circles in the Vessel Contours and Application to Local Probability Density Estimation
Intra-coronary Stent Localization in Intravascular Ultrasound Sequences, A Preliminary Study
Robust Automatic Graph-Based Skeletonization of Hepatic Vascular Trees
DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach
Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network
VOIDD: Automatic Vessel-of-Intervention Dynamic Detection in PCI Procedures
2. Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017
Exploring the Similarity of Medical Imaging Classification Problems
Real Data Augmentation for Medical Image Classification
Detecting and Classifying Nuclei on a Budget
Towards an Efficient Way of Building Annotated Medical Image Collections for Big Data Studies
Crowdsourcing Labels for Pathological Patterns in CT Lung Scans: Can Non-experts Contribute Expert-Quality Ground Truth?
Expected Exponential Loss for Gaze-Based Video and Volume Ground Truth Annotation
SwifTree: Interactive Extraction of 3D Trees Supporting Gaming and Crowdsourcing
Crowdsourced Emphysema Assessment
A Web-Based Platform for Distributed Annotation of Computerized Tomography Scans
Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images
Uncertainty Driven Multi-loss Fully Convolutional Networks for Histopathology

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