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Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

Overview of attention for book
Cover of 'Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis'

Table of Contents

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    Book Overview
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    Chapter 1 Robust Detection of Circles in the Vessel Contours and Application to Local Probability Density Estimation
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    Chapter 2 Intra-coronary Stent Localization in Intravascular Ultrasound Sequences, A Preliminary Study
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    Chapter 3 Robust Automatic Graph-Based Skeletonization of Hepatic Vascular Trees
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    Chapter 4 DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach
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    Chapter 5 Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network
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    Chapter 6 VOIDD: Automatic Vessel-of-Intervention Dynamic Detection in PCI Procedures
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    Chapter 7 Exploring the Similarity of Medical Imaging Classification Problems
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    Chapter 8 Real Data Augmentation for Medical Image Classification
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    Chapter 9 Detecting and Classifying Nuclei on a Budget
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    Chapter 10 Towards an Efficient Way of Building Annotated Medical Image Collections for Big Data Studies
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    Chapter 11 Crowdsourcing Labels for Pathological Patterns in CT Lung Scans: Can Non-experts Contribute Expert-Quality Ground Truth?
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    Chapter 12 Expected Exponential Loss for Gaze-Based Video and Volume Ground Truth Annotation
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    Chapter 13 SwifTree: Interactive Extraction of 3D Trees Supporting Gaming and Crowdsourcing
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    Chapter 14 Crowdsourced Emphysema Assessment
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    Chapter 15 A Web-Based Platform for Distributed Annotation of Computerized Tomography Scans
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    Chapter 16 Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images
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    Chapter 17 Uncertainty Driven Multi-loss Fully Convolutional Networks for Histopathology
Overall attention for this book and its chapters
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Mentioned by

8 tweeters


6 Dimensions

Readers on

50 Mendeley
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Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-67534-3
978-3-31-967533-6, 978-3-31-967534-3

M. Jorge Cardoso, Tal Arbel, Su-Lin Lee, Veronika Cheplygina, Simone Balocco, Diana Mateus, Guillaume Zahnd, Lena Maier-Hein, Stefanie Demirci, Eric Granger, Luc Duong, Marc-André Carbonneau, Shadi Albarqouni, Gustavo Carneiro

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 24%
Student > Ph. D. Student 9 18%
Researcher 8 16%
Student > Doctoral Student 6 12%
Student > Bachelor 1 2%
Other 4 8%
Unknown 10 20%
Readers by discipline Count As %
Computer Science 15 30%
Engineering 14 28%
Medicine and Dentistry 3 6%
Mathematics 2 4%
Physics and Astronomy 1 2%
Other 3 6%
Unknown 12 24%