Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Name: Understanding and Interpreting Machine Learning in Medical Image Computing Applications | First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings
Brand: Springer
Price: 53.49 EUR
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First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings

Danail Stoyanov Zeike Taylor Seyed Mostafa Kia Ipek Oguz Mauricio Reyes Anne Martel Lena Maier-Hein Andre F. Marquand Edouard Duchesnay Tommy Löfstedt Bennett Landman M. Jorge Cardoso Carlos A. Silva Sergio Pereira Raphael Meier(Herausgeber*in)

Springer (Verlag)

Erschienen am 23. Oktober 2018

XVI, 149 Seiten

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978-3-030-02628-8 (ISBN)

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Beschreibung

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Inhalt

Intro
Additional Workshop Editors
MLCN 2018 Preface
DLF 2018 Preface
iMIMIC 2018 Preface
Organization
Contents
First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018
Alzheimer's Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
1 Introduction
2 Method
3 Results
3.1 Benchmark on Synthetic Data
3.2 Application on Real Data
4 Conclusion
References
Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
1 Introduction
2 Method
2.1 Multi-channel Variational Inference
2.2 Gaussian Linear Case
3 Experiments
3.1 Experiments on Linearly Generated Synthetic Datasets
3.2 Application to Clinical and Medical Imaging Data in AD
4 Discussion and Conclusion
References
Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer's Disease
1 Introduction
2 Related Work
2.1 Alzheimer Classification
2.2 Visualization Methods
3 Methods
3.1 Data
3.2 Model
3.3 Visualization Methods
4 Results
4.1 Classification
4.2 Relevant Brain Areas
4.3 Differences Between Visualization Methods
5 Conclusion
References
Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data
1 Introduction
1.1 Multi-site Data and Batch Effects
2 Machine Learning and Functional Connectivity Graphs
3 Batch Effects Correction Techniques
3.1 Adding Site as Covariate
3.2 Z-Score Normalization
3.3 Whitening
3.4 Solving Linear Transformations
4 Experiments and Results
4.1 Dataset
4.2 Experiments and Results
5 Discussion
References
First International Workshop on Deep Learning Fails Workshop, DLF 2018
Towards Robust CT-Ultrasound Registration Using Deep Learning Methods
1 Introduction
2 Methods
3 Data
3.1 Clinical Data
3.2 Training Data
4 Experiments
4.1 Mono-Modal
4.2 Multi-modal (Simulated)
4.3 Inaccurate Ground Truth
4.4 CT-US
5 Discussion and Conclusion
References
To Learn or Not to Learn Features for Deformable Registration?
1 Introduction
2 Method
2.1 Discrete Optimization
2.2 Deep Learning Framework
3 Experiments and Results
3.1 Datasets Description
3.2 Evaluation Metric
3.3 Implementation Detail
3.4 Feature Learning Experiments and Results
4 Conclusions
References
Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning
1 Introduction
2 Methods
2.1 Network Architecture
2.2 Training Details
3 Experiments
3.1 Synthetic Dataset
3.2 Comparison Method: Data-Driven Gating
3.3 Assessment of Corrected Volume Quality
4 Discussion and Conclusions
References
Exploring Adversarial Examples
1 Introduction
2 Related Work
3 Methods
3.1 Dataset
3.2 Training
3.3 Adversarial Data Creation
4 Results
5 Discussion and Conclusion
References
Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks
1 Introduction
2 Methods
2.1 Data and Preprocessing
2.2 Ventricle Segmentation Network
2.3 Training Procedure
3 Experiments and Results
4 Discussion and Conclusions
References
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks
1 Introduction
2 Methods
2.1 Applied Deep Networks
2.2 Applied Adversarial Attacks
3 Dataset
4 Results and Discussion
5 Conclusion
References
First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
1 Introduction
2 Methods
2.1 Triplet-Loss with Global Average Pooling
2.2 Hierarchical Triplet Selection Logic
2.3 Experimental Design
3 Results
4 Conclusion
References
Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment
1 Introduction
2 Methods
2.1 Extraction of the Region of Interest
2.2 Glioma Grading CNN
2.3 Grade Prediction Interpretability
3 Experimental Setup
4 Results and Discussion
5 Conclusion
References
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
1 Introduction
2 Related Work
3 Architecture and Training
4 Feature Map Visualization
5 Conclusion
References
Regression Concept Vectors for Bidirectional Explanations in Histopathology
1 Introduction
2 Methods
2.1 Correlation to Network Prediction
2.2 Regression Concept Vectors
2.3 Sensitivity to RCV
2.4 Evaluation of the Explanations
3 Experiments and Results
3.1 Datasets
3.2 Network Architecture and Training
3.3 Results
4 Discussion and Future Work
References
Towards Complementary Explanations Using Deep Neural Networks
1 Introduction
1.1 Satisfying the Curiosity of Decision Makers
2 Complementary Explanations Using Deep Neural Networks
3 The Three Cs of Interpretability
4 Experimental Assessment
5 Conclusion
References
How Users Perceive Content-Based Image Retrieval for Identifying Skin Images
1 Introduction
2 Method
2.1 Study Design
2.2 Dataset
2.3 System Description
2.4 Protocol
2.5 Data Collection
3 Results
4 Discussion
References
Author Index

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