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BL
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859832
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| Main Entry
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MLCN (Workshop)(1st :2018 :, Granada, Spain)
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| Title & Author
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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 /\ Danail Stoyanov, Zeike Taylor, Seyed Mostafa Kia, Ipek Oguz, Mauricio Reyes et al. (eds.).
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| Publication Statement
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Cham, Switzerland :: Springer,, [2018]
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| Series Statement
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Lecture notes in computer science ;; 11038
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LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
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| Page. NO
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1 online resource (xvi, 148 pages) :: illustrations
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| ISBN
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3030026280
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: 9783030026288
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3030026272
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3030026299
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9783030026271
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9783030026271
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9783030026295
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| Notes
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International conference proceedings.
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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
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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
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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
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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
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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
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| Abstract
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This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
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| Subject
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Computer-assisted surgery, Congresses.
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Diagnostic imaging-- Data processing, Congresses.
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Image Interpretation, Computer-Assisted.
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Artificial intelligence.
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Computer-assisted surgery.
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Computers-- Computer Graphics.
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Computers-- Computer Science.
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Computers-- Intelligence (AI) Semantics.
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Computers-- Programming-- Algorithms.
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Diagnostic imaging-- Data processing.
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Health safety aspects of IT.
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Image processing.
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Life sciences: general issues.
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Mathematical theory of computation.
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Mathematics-- Logic.
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Medical-- General.
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| Dewey Classification
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616.07/57
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| LC Classification
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RC78.7.D53I58 2018
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| NLM classification
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WN 182
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| Added Entry
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Kia, Seyed Mostafa
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Oguz, Ipek
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Reyes, Mauricio
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Stoyanov, Danail
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Taylor, Zeike
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| Added Entry
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DLF (Workshop)(1st :2018 :, Granada, Spain)
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iMIMIC (Workshop)(1st :2018 :, Granada, Spain)
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International Conference on Medical Image Computing and Computer-Assisted Intervention(21st :2018 :, Granada, Spain)
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| Parallel Title
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DLF 2018
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: IMIMIC 2018
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: MLCN 2018
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