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Simplifying Medical Ultrasound

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Cover of 'Simplifying Medical Ultrasound'

Table of Contents

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    Book Overview
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    Chapter 1 Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction
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    Chapter 2 Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures
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    Chapter 3 RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging
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    Chapter 4 Prediction of Kidney Transplant Function with Machine Learning from Computational Ultrasound Features
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    Chapter 5 Differential Learning from Sparse and Noisy Labels for Robust Detection of Clinical Landmarks in Echo Cine Series
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    Chapter 6 End-to-End Myocardial Infarction Classification from Echocardiographic Scans
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    Chapter 7 View Classification of Color Doppler Echocardiography via Automatic Alignment Between Doppler and B-Mode Imaging
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    Chapter 8 AI-Enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
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    Chapter 9 Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D Echo Views
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    Chapter 10 Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography
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    Chapter 11 Adnexal Mass Segmentation with Ultrasound Data Synthesis
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    Chapter 12 Self-Knowledge Distillation for First Trimester Ultrasound Saliency Prediction
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    Chapter 13 A Universal End-to-End Description of Pulse-Echo Ultrasound Image Reconstruction
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    Chapter 14 Learning Generalized Non-rigid Multimodal Biomedical Image Registration from Generic Point Set Data
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    Chapter 15 Contact Force Prediction for a Robotic Transesophageal Ultrasound Probe via Operating Torque Sensing
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    Chapter 16 Meta-registration: Learning Test-Time Optimization for Single-Pair Image Registration
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    Chapter 17 Automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound Images
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    Chapter 18 Towards Multi-modal Self-supervised Video and Ultrasound Pose Estimation for Laparoscopic Liver Surgery
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Title
Simplifying Medical Ultrasound
Published by
Springer International Publishing, September 2022
DOI 10.1007/978-3-031-16902-1
ISBNs
978-3-03-116901-4, 978-3-03-116902-1
Editors

Aylward, Stephen, Noble, J. Alison, Hu, Yipeng, Lee, Su-Lin, Baum, Zachary, Min, Zhe

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