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221004 ||| eng |
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|a 9783031169199
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| 100 |
1 |
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|a Rekik, Islem
|e [editor]
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| 245 |
0 |
0 |
|a Predictive Intelligence in Medicine
|h Elektronische Ressource
|b 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
|c edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Cham
|b Springer Nature Switzerland
|c 2022, 2022
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| 300 |
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|a XI, 213 p. 70 illus., 62 illus. in color
|b online resource
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| 505 |
0 |
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|a 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
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computers
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| 653 |
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|a Application software
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| 653 |
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|a Computer networks
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| 653 |
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|a Computer Engineering and Networks
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Computer engineering
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| 653 |
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|a Computer and Information Systems Applications
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| 653 |
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|a Computing Milieux
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| 700 |
1 |
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|a Adeli, Ehsan
|e [editor]
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| 700 |
1 |
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|a Park, Sang Hyun
|e [editor]
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| 700 |
1 |
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|a Cintas, Celia
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Computer Science
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-16919-9
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-16919-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a 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
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