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Computer-Assisted and Robotic Endoscopy

Overview of attention for book
Cover of 'Computer-Assisted and Robotic Endoscopy'

Table of Contents

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    Book Overview
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    Chapter 1 Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features
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    Chapter 2 Probe Tracking and Its Application in Automatic Acquisition Using a Trans-Esophageal Ultrasound Robot
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    Chapter 3 Hybrid Tracking and Matching Algorithm for Mosaicking Multiple Surgical Views
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    Chapter 4 Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound
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    Chapter 5 Extended Multi-resolution Local Patterns - A Discriminative Feature Learning Approach for Colonoscopy Image Classification
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    Chapter 6 Evaluation of i-Scan Virtual Chromoendoscopy and Traditional Chromoendoscopy for the Automated Diagnosis of Colonic Polyps
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    Chapter 7 ORBSLAM-Based Endoscope Tracking and 3D Reconstruction
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    Chapter 8 Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking
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    Chapter 9 Weakly-Supervised Lesion Detection in Video Capsule Endoscopy Based on a Bag-of-Colour Features Model
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    Chapter 10 Convolutional Neural Network Architectures for the Automated Diagnosis of Celiac Disease
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    Chapter 11 A System for Augmented Reality Guided Laparoscopic Tumour Resection with Quantitative Ex-vivo User Evaluation
Attention for Chapter 10: Convolutional Neural Network Architectures for the Automated Diagnosis of Celiac Disease
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Citations

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21 Mendeley
Summary Dimensions citations
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Chapter title
Convolutional Neural Network Architectures for the Automated Diagnosis of Celiac Disease
Chapter number 10
Book title
Computer-Assisted and Robotic Endoscopy
Published by
Springer, Cham, October 2016
DOI 10.1007/978-3-319-54057-3_10
Book ISBNs
978-3-31-954056-6, 978-3-31-954057-3
Authors

G. Wimmer, S. Hegenbart, A. Vecsei, A. Uhl, Wimmer, G., Hegenbart, S., Vecsei, A., Uhl, A.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Student > Master 3 14%
Student > Bachelor 2 10%
Other 2 10%
Professor > Associate Professor 2 10%
Other 1 5%
Unknown 5 24%
Readers by discipline Count As %
Engineering 4 19%
Computer Science 4 19%
Business, Management and Accounting 2 10%
Mathematics 1 5%
Nursing and Health Professions 1 5%
Other 2 10%
Unknown 7 33%

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