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001450289 001__ 1450289
001450289 003__ OCoLC
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001450289 019__ $$a1347362038
001450289 020__ $$a9783031178993$$q(electronic bk.)
001450289 020__ $$a3031178998$$q(electronic bk.)
001450289 020__ $$z9783031178986
001450289 020__ $$z303117898X
001450289 0247_ $$a10.1007/978-3-031-17899-3$$2doi
001450289 035__ $$aSP(OCoLC)1347378075
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001450289 050_4 $$aQ325.5
001450289 08204 $$a006.3/1$$223/eng/20221013
001450289 1112_ $$aMLCN (Workshop)$$n(5th :$$d2022 :$$cSingapore).
001450289 24510 $$aMachine learning in clinical neuroimaging :$$b5th international workshop, MLCN 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings /$$cAhmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Thomas Wolfers (eds.).
001450289 24630 $$aMLCN 2022
001450289 264_1 $$aCham :$$bSpringer,$$c[2022]
001450289 264_4 $$c©2022
001450289 300__ $$a1 online resource (xi, 180 pages) :$$billustrations (chiefly color).
001450289 336__ $$atext$$btxt$$2rdacontent
001450289 337__ $$acomputer$$bc$$2rdamedia
001450289 338__ $$aonline resource$$bcr$$2rdacarrier
001450289 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13596
001450289 500__ $$aInternational conference proceedings.
001450289 500__ $$aIncludes author index.
001450289 5050_ $$aMorphometry -- Joint Reconstruction and Parcellation of Cortical Surfaces -- A Study of Demographic Bias in CNN-based Brain MR Segmentation -- Volume is All You Need: Improving Multi-task Multiple Instance Learning for WMH Segmentation and Severity Estimation -- Self-Supervised Test-Time Adaptation for Medical Image Segmentation -- Accurate Hippocampus Segmentation Based on Self-Supervised Learning with Fewer Labeled Data -- Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-based Network -- Weakly Supervised Intracranial Hemorrhage Segmentation using Hierarchical Combination of Attention Maps from a Swin Transformer -- Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma -- Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-protocol Volumetric Consistency -- Diagnostics, Aging, and Neurodegeneration -- Non-parametric ODE-based Disease Progression Model of Brain Biomarkers in Alzheimer's Disease -- Lifestyle Factors that Promote Brain Structural Resilience in Individuals with Genetic Risk Factors for Dementia -- Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging -- Augmenting Magnetic Resonance Imaging with Tabular Features for Enhanced and Interpretable Medial Temporal Lobe Atrophy Prediction -- Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury -- Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning? -- fMRI-S4: Learning Short- and Long-range Dynamic fMRI Dependencies Using 1D Convolutions and State Space Models -- Data Augmentation via Partial Nonlinear Registration for Brain-age Prediction.
001450289 506__ $$aAccess limited to authorized users.
001450289 520__ $$aThis book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions. The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration. .
001450289 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 13, 2022).
001450289 650_0 $$aDiagnostic imaging$$xDigital techniques$$vCongresses.
001450289 650_0 $$aMachine learning$$vCongresses.
001450289 650_0 $$aElectronic data processing$$xDistributed processing$$vCongresses.
001450289 655_0 $$aElectronic books.
001450289 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001450289 655_7 $$aConference papers and proceedings.$$2lcgft
001450289 7001_ $$aAbdulkadir, Ahmed,$$eeditor.
001450289 7001_ $$aBathula, Deepti R.,$$eeditor.
001450289 7001_ $$aDvornek, Nicha C.,$$eeditor.
001450289 7001_ $$aHabes, Mohamad,$$eeditor.
001450289 7001_ $$aKia, Seyed Mostafa,$$eeditor.
001450289 7001_ $$aKumar, Vinod,$$eeditor.
001450289 7001_ $$aWolfers, Thomas,$$eeditor.
001450289 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(25th :$$d2022 :$$cSingapore)
001450289 77608 $$iPrint version:$$z303117898X$$z9783031178986$$w(OCoLC)1342984173
001450289 830_0 $$aLecture notes in computer science ;$$v13596.$$x1611-3349
001450289 852__ $$bebk
001450289 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-17899-3$$zOnline Access$$91397441.1
001450289 909CO $$ooai:library.usi.edu:1450289$$pGLOBAL_SET
001450289 980__ $$aBIB
001450289 980__ $$aEBOOK
001450289 982__ $$aEbook
001450289 983__ $$aOnline
001450289 994__ $$a92$$bISE