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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

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Cover of 'Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease
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    Chapter 2 A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN
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    Chapter 3 Introduction to Binary Coordinate Ascent: New Insights into Efficient Feature Subset Selection for Machine Learning
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    Chapter 4 Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography
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    Chapter 5 Detecting Mammographic Masses via Image Retrieval and Discriminative Learning
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    Chapter 6 High-Order Statistics of Micro-Texton for HEp-2 Staining Pattern Classification
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    Chapter 7 Intelligent Diagnosis of Breast Cancer Based on Quantitative B-Mode and Elastography Features
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    Chapter 8 Categorization of Lung Tumors into Benign/Malignant, Solid/GGO, and Typical Benign/Others
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    Chapter 9 Fuzzy Object Growth Model for Neonatal Brain MR Understanding
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    Chapter 10 Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis
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    Chapter 11 Radiomics in Medical Imaging—Detection, Extraction and Segmentation
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    Chapter 12 Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques
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    Chapter 13 Image Guided and Robot Assisted Precision Surgery
Attention for Chapter 1: Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease
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Citations

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Chapter title
Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease
Chapter number 1
Book title
Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-68843-5_1
Book ISBNs
978-3-31-968842-8, 978-3-31-968843-5
Authors

Daoqiang Zhang, Chen Zu, Biao Jie, Tingting Ye

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 > Master 2 40%
Student > Postgraduate 1 20%
Student > Bachelor 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 1 20%
Psychology 1 20%
Engineering 1 20%
Unknown 2 40%