PRedictive Intelligence in MEdicine

First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
1. Auflage 2018
ISBN: 978-3-030-00319-7
Verlag: Springer International Publishing

Buch, Englisch, Band 11121, 174 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 295 g

Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics

This book constitutes the refereed proceedings of the First International Workshop on PRedictive Intelligence in MEdicine, PRIME 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.

The 20 full papers presented were carefully reviewed and selected from 23 submissions. The main aim of the workshop is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field.

Zielgruppe

Research

Autoren/Hrsg.

Rekik, Islem

Park, Sang Hyun

Unal, Gozde

Adeli, Ehsan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik

Inhaltsverzeichnis

Computer Aided Identification of Motion Disturbances Related to Parkinson's Disease.- Prediction of Severity and Treatment Outcome for ASD from fMRI.- Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network.- Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer's Disease.- Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study.- Predictive Patient Care: Survival Model to Prevent Medication Non-adherence.- Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-Modality Data.- Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease.- Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations.- Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis.- Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI.- Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning.- Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes.- Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs.- Towards Continuous Health Diagnosis from Faces with Deep Learning.- XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference.- 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer's Disease Diagnosis.- Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI.- Diffusion MRI Spatial Super-Resolution Using Generative Adversarialv Networks.- Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks.