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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
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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging
Published by
Springer International Publishing, January 2018
DOI 10.1007/978-3-319-68843-5
978-3-31-968842-8, 978-3-31-968843-5

Suzuki, Kenji, Chen, Yisong

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Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 10%
Student > Ph. D. Student 2 6%
Other 1 3%
Unknown 25 81%
Readers by discipline Count As %
Computer Science 2 6%
Medicine and Dentistry 1 3%
Engineering 1 3%
Unknown 27 87%