001439925 000__ 05259cam\a2200745\i\4500
001439925 001__ 1439925
001439925 003__ OCoLC
001439925 005__ 20230309004530.0
001439925 006__ m\\\\\o\\d\\\\\\\\
001439925 007__ cr\un\nnnunnun
001439925 008__ 210926s2021\\\\sz\a\\\\o\\\\\101\0\eng\d
001439925 019__ $$a1269096747$$a1269202087
001439925 020__ $$a9783030875862$$q(electronic bk.)
001439925 020__ $$a3030875865$$q(electronic bk.)
001439925 020__ $$z9783030875855
001439925 020__ $$z3030875857
001439925 0247_ $$a10.1007/978-3-030-87586-2$$2doi
001439925 035__ $$aSP(OCoLC)1269095956
001439925 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dOCLCQ$$dOCLCO$$dOCLCQ
001439925 049__ $$aISEA
001439925 050_4 $$aQ325.5$$b.M53 2021
001439925 08204 $$a006.3/1$$223
001439925 1112_ $$aMLCN (Workshop)$$n(4th :$$d2021 :$$cOnline)
001439925 24510 $$aMachine learning in clinical neuroimaging :$$b4th international conference, MLCN 2021 : held in conjunction with MICCAI 2021 : Strasbourg, France, September 27, 2021 : proceedings /$$cAhmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers (eds.).
001439925 24630 $$aMLCN 2021
001439925 264_1 $$aCham :$$bSpringer,$$c[2021]
001439925 264_4 $$c©2021
001439925 300__ $$a1 online resource :$$billustrations (chiefly color)
001439925 336__ $$atext$$btxt$$2rdacontent
001439925 337__ $$acomputer$$bc$$2rdamedia
001439925 338__ $$aonline resource$$bcr$$2rdacarrier
001439925 4901_ $$aLecture notes in computer science ;$$v13001
001439925 4901_ $$aLNCS sublibrary: SL6 - Image processing, computer vision, pattern recognition, and graphics
001439925 500__ $$aInternational conference proceedings.
001439925 500__ $$aIncludes author index.
001439925 5050_ $$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.
001439925 506__ $$aAccess limited to authorized users.
001439925 520__ $$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.
001439925 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 1, 2021).
001439925 650_0 $$aDiagnostic imaging$$xDigital techniques$$vCongresses.
001439925 650_0 $$aMachine learning$$vCongresses.
001439925 650_0 $$aElectronic data processing$$xDistributed processing$$vCongresses.
001439925 650_6 $$aImagerie pour le diagnostic$$xTechniques numériques$$vCongrès.
001439925 650_6 $$aApprentissage automatique$$vCongrès.
001439925 650_6 $$aTraitement réparti$$vCongrès.
001439925 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001439925 655_7 $$aConference papers and proceedings.$$2lcgft
001439925 655_7 $$aActes de congrès.$$2rvmgf
001439925 655_0 $$aElectronic books.
001439925 7001_ $$aAbdulkadir, Ahmed,$$eeditor.
001439925 7001_ $$aKia, Seyed Mostafa,$$eeditor.
001439925 7001_ $$aHabes, Mohamad,$$eeditor.
001439925 7001_ $$aKumar, Vinod,$$eeditor.
001439925 7001_ $$aRondina, Jane Maryam,$$eeditor.
001439925 7001_ $$aTax, Chantal,$$eeditor.
001439925 7001_ $$aWolfers, Thomas,$$eeditor.
001439925 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(23rd :$$d2020 :$$cOnline)
001439925 830_0 $$aLecture notes in computer science ;$$v13001.
001439925 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001439925 852__ $$bebk
001439925 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-87586-2$$zOnline Access$$91397441.1
001439925 909CO $$ooai:library.usi.edu:1439925$$pGLOBAL_SET
001439925 980__ $$aBIB
001439925 980__ $$aEBOOK
001439925 982__ $$aEbook
001439925 983__ $$aOnline
001439925 994__ $$a92$$bISE