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Machine Learning for Multimodal Healthcare Data

Overview of attention for book
Cover of 'Machine Learning for Multimodal Healthcare Data'

Table of Contents

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    Book Overview
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    Chapter 1 Death Prediction by Race in Colorectal Cancer Patients Using Machine Learning Approaches
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    Chapter 2 Neural Graph Revealers
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    Chapter 3 Multi-modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video
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    Chapter 4 RobustSsF: Robust Missing Modality Brain Tumor Segmentation with Self-supervised Learning-Based Scenario-Specific Fusion
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    Chapter 5 Semi-supervised Cooperative Learning for Multiomics Data Fusion
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    Chapter 6 Exploiting Partial Common Information Microstructure for Multi-modal Brain Tumor Segmentation
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    Chapter 7 Multimodal LLMs for Health Grounded in Individual-Specific Data
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    Chapter 8 Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders
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    Chapter 9 HOOREX: Higher Order Optimizers for 3D Recovery from X-Ray Images
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    Chapter 10 GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
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    Chapter 11 MaxCorrMGNN: A Multi-graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
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    Chapter 12 SIM-CNN: Self-supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals
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    Chapter 13 InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data
Attention for Chapter 13: InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data
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Chapter title
InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data
Chapter number 13
Book title
Machine Learning for Multimodal Healthcare Data
Published by
Springer, Cham, January 2024
DOI 10.1007/978-3-031-47679-2_13
Book ISBNs
978-3-03-147678-5, 978-3-03-147679-2
Authors

Giles, Dominic, Gray, Robert, Foulon, Chris, Pombo, Guilherme, Xu, Tianbo, Ruffle, James K., Jäger, H. Rolf, Cardoso, Jorge, Ourselin, Sebastien, Rees, Geraint, Jha, Ashwani, Nachev, Parashkev

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Unknown 1 100%

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