UID:
almahu_9949372070202882
Umfang:
XI, 213 p. 70 illus., 62 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2022.
ISBN:
9783031169199
Serie:
Lecture Notes in Computer Science, 13564
Inhalt:
This book constitutes the proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with MICCAI 2022 as a hybrid event in Singapore, in September 2022. The 19 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
Anmerkung:
Federated Time-dependent GNN Learning from Brain Connectivity Data with Missing Timepoints -- Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing -- Multi-Tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas -- Multiple Instance Neuroimage Transformer -- Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach -- Mixup augmentation improves age prediction from T1-weighted brain MRI scans -- Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning -- MISS-Net: Multi-view contrastive transformer network for MCI stages prediction using brain 18F-FDG PET imaging -- TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation -- Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study -- Weakly-Supervised TILs Segmentation based on Point Annotations using Transfer Learning with Point Detector and Projected-Boundary Regressor -- Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage -- Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-Task Learning on Imaging and Tabular Data -- Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts -- Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets -- Learning subject-specific functional parcellations from cortical surface measures -- A Triplet Contrast Learning of Global and Local Representations for Unannotated Medical Images -- Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification -- Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031169182
Weitere Ausg.:
Printed edition: ISBN 9783031169205
Sprache:
Englisch
DOI:
10.1007/978-3-031-16919-9
URL:
https://doi.org/10.1007/978-3-031-16919-9
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