Machine Learning in Medical Imaging

Name: Machine Learning in Medical Imaging | 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings
Brand: Springer
Price: 53.49 EUR
Availability: OnlineOnly

7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings

Li Wang Ehsan Adeli Qian Wang Yinghuan Shi Heung-Il Suk(Editor)

Springer (Publisher)

Published on 10. October 2016

XIV, 324 pages

E-Book

PDF with digital watermarking

System requirements

978-3-319-47157-0 (ISBN)

€53.49incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Content

Intro
Preface
Organization
Contents
Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph
Abstract
1 Introduction
2 Method
2.1 Encode Subject-Wise Relationship in Hypergraph
2.2 Discover High Order Brain Connectome Patterns by Hypergraph Learning
3 Experiments
3.1 Critical Subnetworks Learned by Hypergraph Inference
3.2 Identification of ASD Subjects with the Learned Subnetwork
4 Conclusion
References
Bilateral Regularization in Reproducing Kernel Hilbert Spaces for Discontinuity Preserving Image Registration
1 Introduction
2 Background
3 Method
3.1 Bilateral Regularizer
3.2 Anisotropic Kernel
4 Results
5 Conclusion
References
Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection
1 Introduction
1.1 Contribution
2 Experimental Study
2.1 Image Dataset
2.2 Proposed Architecture
2.3 Image Representations and Evaluation Details
2.4 Fraction of Mislabeled Training Data
2.5 Variability in Positive and Negative Class
2.6 Detection Results
3 Discussion
References
Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates
1 Introduction
2 Method
2.1 Regional Learning-Based Segmentation
2.2 Ensemble Segmentation Based on Probabilistic Estimates
3 Experiments
3.1 IBSR Dataset
3.2 NeoBrainS12 Challenge
4 Conclusions
References
Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life
Abstract
1 Introduction
2 Methods
2.1 Integrated Random Forest Regression and Auto-Context Model
2.2 Register New Infant Image with Two Learned Models
3 Experiments
4 Conclusion
References
Detecting Osteophytes in Radiographs of the Knee to Diagnose Osteoarthritis
1 Introduction
2 Background
2.1 Osteoarthritis Grading
2.2 Automated Methods
2.3 Shape Modelling and Matching
3 Methods
3.1 Osteophyte Features
4 Experiments
4.1 Data
4.2 Classification Experiments
4.3 Osteophyte Detection
4.4 OA vs Non-OA
4.5 Multi-class Experiments
5 Discussion and Conclusion
References
Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging
1 Introduction
2 Material
3 Deep Learning
4 Using CNN for Estimating the fODF
4.1 Class Definition
4.2 Pre-processing
4.3 The Architecture
5 Results
6 Discussion
7 Conclusion
References
Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image
1 Introduction
2 Method
2.1 Region-of-Interest Generation
2.2 Voxel Sampling Strategy
2.3 Feature Extraction
2.4 Structured Random Forest
3 Experiments, Results and Discussions
4 Conclusion
References
Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images
Abstract
1 Introduction
2 Method
2.1 Groupwise Time-Serial Image Registration
2.2 Manifold-Guided Registration of Atlases and Subject Image Sequence
2.3 Consistent ROI Labeling of Longitudinal Image Sequence
3 Experimental Results
3.1 Evaluation of Longitudinal Labeling Consistency
3.2 Evaluation of Longitudinal Labeling Accuracy
4 Conclusion
References
Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis
1 Introduction
2 Method
2.1 Task-Oriented Supervised Feature Selection
2.2 Representation-Oriented Unsupervised Feature Selection
2.3 Proposed Objective Function
3 Experimental Results
3.1 Classification Results
3.2 Most Discriminative Brain Regions
4 Conclusion
References
Patch-Based Hippocampus Segmentation Using a Local Subspace Learning Method
Abstract
1 Introduction
2 Method
2.1 Multi-atlas Based Segmentation Method
2.2 Local Subspace Learning Based Patch-Wise Label Propagation Method
3 Experiments
4 Conclusion
References
Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework
Abstract
1 Introduction
2 Boosted-LUPI-Based Classification Framework
2.1 Random Subspace Feature Sampling
2.2 Privileged Information Learning
2.3 Multiple Kernel Boosting Learning
3 Experiment and Results
3.1 Experiment on MLSP Schizophrenia Dataset
3.2 Experiment on ADNI Dataset
4 Conclusion
Acknowledgments
References
A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation
1 Introduction
2 Unsupervised One-Class Learning
3 Semi-supervised Large Margin Algorithm
3.1 Learning Model
3.2 Algorithm
4 Results
5 Conclusion
References
Deep Ensemble Sparse Regression Network for Alzheimer's Disease Diagnosis
1 Introduction
2 Materials and Image Processing
3 Deep Ensemble Sparse Regression Network
3.1 Sparse Linear/Logistic Regression
3.2 Deep Convolutional Neural Network for Classification
4 Experimental Settings and Results
4.1 Experimental Settings
4.2 Performance Comparison
5 Conclusion
References
Learning Representation for Histopathological Image with Quaternion Grassmann Average Network
Abstract
1 Introduction
2 Quaternion Grassmann Average Network
2.1 Quaternion Grassmann Average Algorithm
2.2 QGA Network (QGANet)
2.3 QGANet-Based Classification Framework
3 Experiment and Results
3.1 Experiments
3.2 Results
4 Discussion and Conclusion
Acknowledgments
References
Learning Global and Cluster-Specific Classifiers for Robust Brain Extraction in MR Data
1 Introduction
2 Methods
2.1 Supervised Learning via Global Random Forests
2.2 Learning Cluster-Specific Classifiers
3 Results
4 Discussions
References
Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations and Atlas Location Autocontext
1 Introduction
2 Methodology
3 Experiments
4 Conclusion
References
Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection
1 Introduction
2 Methods
2.1 CNN Architecture for Multi-label ILD Regression
2.2 Unordered Pooling Regression via Fisher Vector Encoding
3 Experiments and Discussion
4 Conclusion
References
Segmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests
1 Introduction
2 Methods
2.1 Image Representation
2.2 Learning and Prediction
2.3 Kidney Volume Estimation
3 Validation Setup
4 Results
5 Conclusions
References
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers
1 Introduction
2 Methods
3 Results
4 Conclusions
References
Retinal Image Quality Classification Using Saliency Maps and CNNs
1 Introduction
2 Methods
2.1 Saliency Model
2.2 CNN Architecture
2.3 Training the CNN
2.4 Image Quality Classification
3 Experiments and Results
3.1 Dataset Description
4 Conclusion
References
Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
1 Introduction
2 Model
3 Empirical Results
3.1 Results
4 Conclusions
References
Tree-Based Transforms for Privileged Learning
1 Introduction
2 Method
2.1 Building a Support Forest Using Fm
2.2 Growing Cross-Modality Tree-Based Feature Transforms
2.3 Implementation Details
3 Evaluation Data and Methods
4 Results
5 Conclusions and Future Work
References
Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment
Abstract
1 Introduction
2 Related Work
3 Localization of the Fetus in the Sagittal Plane
3.1 Edge Detection Using Structured Random Forests
3.2 Detection of the Fetus Region-of-Interest
4 Detection of the Best Head and Abdominal Planes
4.1 Fetal-Partitioning via Transfer Learning CNNs
4.2 Detection of Best Plane for Fetal Head and Abdomen Biometry
5 Experiments
6 Results
6.1 Localization of the Fetus in the Sagittal Plane
6.2 Fetal-Partitioning and Extraction of Biometry Planes
7 Discussion and Conclusion
References
Learning for Graph-Based Sensorless Freehand 3D Ultrasound
1 Introduction
2 Method
2.1 Rigid Motion Estimation
2.2 Graph-Based Trajectory Estimation
2.3 Learning to Characterize Measurement Error
3 Experiments and Results
3.1 Data Acquisition
3.2 Evaluation of Probe Trajectory Estimation
4 Conclusion
References
Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling
1 Introduction
2 Materials and Methods
2.1 Proposed Algorithm
2.2 Appearance and Context Features
3 Experiments
3.1 Comparison with Other Methods on 10 Subjects
4 Conclusion
References
Transductive Maximum Margin Classification of ADHD Using Resting State fMRI
Abstract
1 Introduction
2 Method
2.1 Maximum Margin Classification (MMC)
2.2 Smoothness Constraints
2.3 Transductive-MMC
3 Results
4 Conclusion
Acknowledgements
References
Automatic Hippocampal Subfield Segmentation from 3T Multi-modality Images
1 Introduction
2 Motivation and Main Framework
3 Relationship Feature Calculation and Updating
4 Experiments
5 Conclusion
References
Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs
1 Introduction
2 Method
2.1 Main Framework
2.2 Deformation Field Estimation
2.3 Regression Guided Deformable Segmentation
2.4 ROI Merging
3 Experiments
4 Conclusion
References
Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis
1 Introduction
2 Method
2.1 Data Preprocessing and Network Construction
2.2 Dynamical Network Thresholding
2.3 Network Fusion
2.4 Classification
3 Experimental Results
3.1 Experimental Setup
3.2 Classification Performance
4 Conclusion
References
Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement
1 Introduction
2 Related Work
3 System Overview
3.1 Single-Scale Lesion Confidence Map
3.2 Dynamic Rule-Based Refinement
3.3 Multi-scale Fusion
4 Experimental Results
4.1 Datasets
4.2 Performance Measure and Parameters Setting
5 Conclusion
References
Structure Fusion for Automatic Segmentation of Left Atrial Aneurysm Based on Deep Residual Networks
1 Introduction
2 Method
3 Experiments and Results
4 Conclusion
References
Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface
1 Introduction
2 Method
2.1 Machine Learning: Features and Classification
2.2 Convex Segmentation with Learned Likelihoods
3 Results
4 Conclusion
References
Iterative Dual LDA: A Novel Classification Algorithm for Resting State fMRI
1 Introduction
2 Methods
2.1 Proposed Algorithm
2.2 Simulated Data
2.3 Parkinson's Disease Data
3 Results and Discussion
3.1 Simulated Data
3.2 Parkinson's Disease Data
4 Conclusion
References
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
Abstract
1 Introduction
2 Problem Definition
3 Methods
3.1 Cell Detection with Hough Forest
3.2 Joint Detection of Mother and Daughters Using CRF
4 Results
4.1 Experiment and Dataset Description
4.2 Evaluation of Cell Detection
4.3 Evaluation of Mitosis Detection
5 Discussion
References
Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI
Abstract
1 Introduction
2 Multi-resolution Image Analysis
3 Materials and Methods
3.1 DCE MRI Data
3.2 Extraction of Texture Features
3.3 Classification
4 Results
5 Conclusion
Acknowledgements
References
Novel Morphological Features for Non-mass-like Breast Lesion Classification on DCE-MRI
1 Introduction
2 Materials and Methods
2.1 Imaging Technique and Data Set
2.2 Feature Extraction
3 Results and Evaluations
3.1 Classification Results with Feature Selection
4 Conclusion and Discussion
References
Fast Neuroimaging-Based Retrieval for Alzheimer's Disease Analysis
1 Introduction
2 Proposed Framework
2.1 Landmark Detection
2.2 Landmark Selection
2.3 Hashing Construction
2.4 Testing Stage
3 Experiments
3.1 Experimental Results of Landmark Selection
3.2 Experimental Results of Hashing
4 Conclusion
References
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Machine Learning in Medical Imaging

Description

More details

Other editions

Additional editions

Content

System requirements