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Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

1st ed. 2021., 2021

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Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

NeuroImage clinical, 2018-01, Vol.17, p.628-641

2018

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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology: Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings

1, 2020

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Framingham Coronary Heart Disease Risk Score Can be Predicted from Structural Brain Images in Elderly Subjects

Frontiers in aging neuroscience, 2014-12, Vol.6, p.300-300

2014

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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-Oncology Image Processing, Computer Vision, Pattern Recognition, and Graphics: Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction With MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings

1st Edition 2020, 2020

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Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging

4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2021,Strasbourg, France,2021-09-27 - 2021-09-27, 2021, Vol.13001 LNCS, p.3-12

2021

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Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines

Frontiers in neuroscience, 2012-01, Vol.6, p.178-178

2012

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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology: Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

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Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI Data

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Intro

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Organization

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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MRI Image Registration Considerably Improves CNN-Based Disease Classification

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Brain Networks and Time Series

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Computational Anatomy

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

Machine Learning in Clinical Neuroimaging, 2021, Vol.13001

2021

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