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Data Engineering in Medical Imaging

Overview of attention for book
Cover of 'Data Engineering in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Weakly Supervised Medical Image Segmentation Through Dense Combinations of Dense Pseudo-Labels
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    Chapter 2 Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis
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    Chapter 3 A Client-Server Deep Federated Learning for Cross-Domain Surgical Image Segmentation
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    Chapter 4 Pre-training with Simulated Ultrasound Images for Breast Mass Segmentation and Classification
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    Chapter 5 Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
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    Chapter 6 A Self-supervised Approach for Detecting the Edges of Haustral Folds in Colonoscopy Video
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    Chapter 7 Procedurally Generated Colonoscopy and Laparoscopy Data for Improved Model Training Performance
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    Chapter 8 Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining
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    Chapter 9 Investigating Transformer Encoding Techniques to Improve Data-Driven Volume-to-Surface Liver Registration for Image-Guided Navigation
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    Chapter 10 Vision Transformer-Based Self-supervised Learning for Ulcerative Colitis Grading in Colonoscopy
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    Chapter 11 Task-Guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy
Attention for Chapter 5: Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
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Chapter title
Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
Chapter number 5
Book title
Data Engineering in Medical Imaging
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-44992-5_5
Book ISBNs
978-3-03-144991-8, 978-3-03-144992-5
Authors

Denner, Stefan, Scherer, Jonas, Kades, Klaus, Bounias, Dimitrios, Schader, Philipp, Kausch, Lisa, Bujotzek, Markus, Bucher, Andreas Michael, Penzkofer, Tobias, Maier-Hein, Klaus

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Researcher 1 20%
Student > Master 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 3 60%
Psychology 1 20%
Unknown 1 20%

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