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Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures

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Cover of 'Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures'

Table of Contents

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    Book Overview
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    Chapter 1 Optimal Targeting Visualizations for Surgical Navigation of Iliosacral Screws
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    Chapter 2 Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records
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    Chapter 3 A Radiomics-Based Machine Learning Approach to Assess Collateral Circulation in Ischemic Stroke on Non-contrast Computed Tomography
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    Chapter 4 Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement
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    Chapter 5 3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research
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    Chapter 6 Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision
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    Chapter 7 Single-Shot Deep Volumetric Regression for Mobile Medical Augmented Reality
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    Chapter 8 A Baseline Approach for AutoImplant: The MICCAI 2020 Cranial Implant Design Challenge
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    Chapter 9 Adversarial Prediction of Radiotherapy Treatment Machine Parameters
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    Chapter 10 Soft Tissue Sarcoma Co-segmentation in Combined MRI and PET/CT Data
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    Chapter 11 Towards Automated Diagnosis with Attentive Multi-modal Learning Using Electronic Health Records and Chest X-Rays
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    Chapter 12 LUCAS: LUng CAncer Screening with Multimodal Biomarkers
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    Chapter 13 Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound
Attention for Chapter 4: Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement
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Chapter title
Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement
Chapter number 4
Book title
Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-60946-7_4
Book ISBNs
978-3-03-060945-0, 978-3-03-060946-7
Authors

Igor Varga, Eduard Bakstein, Greydon Gilmore, Daniel Novak, Varga, Igor, Bakstein, Eduard, Gilmore, Greydon, Novak, Daniel

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 43%
Researcher 2 29%
Student > Bachelor 1 14%
Unknown 1 14%
Readers by discipline Count As %
Engineering 2 29%
Medicine and Dentistry 1 14%
Computer Science 1 14%
Unknown 3 43%