UID:
almahu_9948691367202882
Umfang:
XII, 212 p. 65 illus.
,
online resource.
Ausgabe:
1st ed. 2020.
ISBN:
9783030593544
Serie:
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12329
Inhalt:
This book constitutes the proceedings of the Second International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually due to the COVID-19 pandemic. The 17 full and 2 short 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:
Context-Aware Synergetic Multiplex Network for Multi-Organ Segmentation of Cervical Cancer MRI -- Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation -- Adversarial Brain Multiplex Prediction From a Single Network for High-Order Connectional Gender-Specific Brain Mapping -- Learned deep radiomics for survival analysis with attention -- Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging -- Joint Clinical Data and CT Image based Prognosis: A Case Study on Postoperative Pulmonary Venous Obstruction Prediction -- Low-Dose CT Denoising using Octave Convolution with High and Low Frequency bands -- Conditional Generative Adversarial Network for Predicting 3D Medical Images Affected by Alzheimer's Diseases -- Inpainting Cropped Diffusion MRI using Deep Generative Models -- Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification -- Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer -- Longitudinal prediction of anatomical changes of parotid glands -- Deep Parametric Mixtures for Modeling the Functional Connectome -- Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint -- Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction -- mr2 NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography -- Template-oriented Multi-task Sparse Low-rank Learning for Parkinson's Diseases Diagnosis -- Multimodal Prediction of Breast Cancer Relapse Prior to Neoadjuvant Chemotherapy Treatment.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783030593537
Weitere Ausg.:
Printed edition: ISBN 9783030593551
Sprache:
Englisch
DOI:
10.1007/978-3-030-59354-4
URL:
https://doi.org/10.1007/978-3-030-59354-4
