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024 7 _a10.1007/978-3-030-87586-2
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245 1 0 _aMachine Learning in Clinical Neuroimaging
_h[electronic resource] :
_b4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
_cedited by Ahmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXI, 176 p. 65 illus., 53 illus. in color.
_bonline resource.
336 _atext
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_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v13001
505 0 _aComputational Anatomy -- Unfolding the medial temporal lobe cortex to characterize neurodegeneration due to Alzheimer's disease pathology using ex vivo imaging -- Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks -- Towards Self-Explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows -- Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients -- MRI image registration considerably improves CNN-based disease classification -- Dynamic Sub-graph Learning for Patch-based Cortical Folding Classification -- Detection of abnormal folding patterns with unsupervised deep generative models -- PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction -- Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network -- Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance -- Brain Networks and Time Series -- Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation -- Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data -- Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling -- Structure-Function Mapping via Graph Neural Networks -- Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity -- H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning -- Constrained Learning of Task-related and Spatially-Coherent Dictionaries from Task fMRI Data.
520 _aThis book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_9120976
650 0 _aArtificial intelligence.
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650 0 _aBioinformatics.
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650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputational and Systems Biology.
_931619
700 1 _aAbdulkadir, Ahmed.
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700 1 _aKia, Seyed Mostafa.
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700 1 _aHabes, Mohamad.
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700 1 _aKumar, Vinod.
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700 1 _aRondina, Jane Maryam.
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700 1 _aTax, Chantal.
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700 1 _aWolfers, Thomas.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
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856 4 0 _uhttps://doi.org/10.1007/978-3-030-87586-2
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