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Machine Learning in Clinical Neuroimaging: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings de Ahmed Abdulkadir

Machine Learning in Clinical Neuroimaging: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings: Lecture Notes in Computer Science, cartea 13596

Editat de Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Thomas Wolfers
en Limba Engleză Paperback – 8 oct 2022
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. 
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Specificații

ISBN-13: 9783031178986
ISBN-10: 303117898X
Pagini: 180
Ilustrații: XI, 180 p. 56 illus., 49 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2022
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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 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.