Predictive Intelligence in Medicine : 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Julia Schnabel

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• Edizione: 1st ed. 2021.
• Descrizione fisica: 1 online resource (292 pages)
• Disciplina: 610.28563
• Soggetto topico: Artificial intelligence; Image processing - Digital techniques; Computer vision; Computer engineering; Computer networks; Bioinformatics; Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Engineering and Networks; Computational and Systems Biology
• Persona (resp. second.): RekikIslem
• Note generali: Includes index.
• Nota di contenuto: Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs -- A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint -- One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction -- Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing -- Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice Prediction: A Physician-Inspired Approach -- Improving Tuberculosis Recognition on Bone-Suppressed Chest X-rays Guided by Task-Specific Features -- Template-Based Inter-modality Super-resolution of Brain Connectivity -- Adversarial Bayesian Optimization for Quantifying Motion Artifact within MRI -- False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-Supervised Learning -- Investigating and Quantifying the Reproducibility of Graph Neural Networks in Predictive Medicine -- Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance -- Self-Guided Multi-Attention Network for Periventricular Leukomalacia Recognition -- Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray -- Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer -- Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network -- Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation – Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution -- A Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis using Ultrasonography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy using Multi-scale Patch Learning with Mammography -- The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification -- Anatomical Structure-aware Pulmonary Nodule Detection via Parallel Multi-Task RoI Head -- Towards Cancer Patients Classification Using Liquid Biopsy -- Foreseeing Survival through `Fuzzy Intelligence': A cognitively-inspired incremental learning based de novo model for Breast Cancer Prognosis by multi-omics data fusion -- Improving Across Dataset Brain Age Predictions using Transfer Learning -- Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation -- FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates.
• Sommario/riassunto: This book constitutes the proceedings of the 4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in October 2021.* The 25 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. *The workshop was held virtually.
• Titolo autorizzato: Predictive Intelligence in Medicine
• ISBN: 3-030-87602-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910503002803321
• Lo trovi qui: Univ. Federico II
• Serie: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 12928