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Record Nr. |
UNINA9910349272903321 |
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Titolo |
Predictive Intelligence in Medicine : Second International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XIII, 178 p. 58 illus., 48 illus. in color.) |
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Collana |
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Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 11843 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Mathematical statistics |
Optical data processing |
Algorithms |
Data mining |
Artificial Intelligence |
Probability and Statistics in Computer Science |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Algorithm Analysis and Problem Complexity |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data -- Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study -- Adaptive Neuro-Fuzzy Inference System-based Chaotic Swarm Intelligence Hybrid Model for Recognition of Mild Cognitive Impairment from Resting-state fMRI -- Deep Learning via Fused Bidirectional Attention Stacked Long Short-term Memory for Obsessive-Compulsive Disorder Diagnosis and Risk Screening -- Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning -- Predicting Response to the Antidepressant Bupropion using |
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Pretreatment fMRI -- Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI using Bidirectionally Supervised Sample Selection -- Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer using a Non-Proportional Hazards Model -- 7 years of Developing Seed Techniques for Alzheimer's Disease Diagnosis using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis -- Generative Adversarial Irregularity Detection in Mammography Images -- Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification From a Source Graph -- Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution Neighborhoods -- Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks -- Prediction of Clinical Scores for Subjective Cognitive Decline and Mild Cognitive Impairment -- Diagnosis of Parkinsons Disease in Genetic Cohort Patients via Stage-wise Hierarchical Deep Polynomial Ensemble learning -- Automatic Detection of Bowel Disease with Residual Networks -- Support Vector based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimers Disease -- Treatment Response Prediction of Hepatocellular Carcinoma Patients from Abdominal CT Images with Deep Convolutional Neural Networks. |
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Sommario/riassunto |
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This book constitutes the proceedings of the Second International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 18 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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