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El. knyga: Machine Learning in Clinical Neuroimaging: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

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  • Formatas: EPUB+DRM
  • Serija: Lecture Notes in Computer Science 13596
  • Išleidimo metai: 07-Oct-2022
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031178993
Kitos knygos pagal šią temą:
Machine Learning in Clinical Neuroimaging: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, ProceedingsDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Serija: Lecture Notes in Computer Science 13596
  • Išleidimo metai: 07-Oct-2022
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031178993
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This 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. 
Morphometry.- 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 Alzheimers 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.
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