Описание: This book is designed to be 1) a resource book on clinical decision support systems for informatics specialists; 2) a textbook for teachers or students in health informatics or medical informatics training programs; and 3) a comprehensive introduction for clinicians, with or without expertise in the applications of computers in medicine, who are interested in learning about current developments in computer-based diagnostic systems. In recent years, it has become obvious that other health professionals, in addition to physicians, have needs for decision support and that the issues raised in this book apply to a broad range of clinicians. The book includes chapters by nationally and internationally recognized experts on the design, evaluation and application of these systems who examine the impact of practitioner and patient use of computer-based diagnostic tools.
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.
The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
Описание: This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019).- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification.- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics.- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis.- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection.- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules.- Deep neural network or dermatologist?.- Towards Interpretability of Segmentation Networks by analyzing DeepDreams.- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019).- Towards Automatic Diagnosis from Multi-modal Medical Data.- Deep Learning based Multi-Modal Registration for Retinal Imaging.- Automated Enriched Medical Concept Generation for Chest X-ray Images.
Описание: CLIP 2020.- Optimal Targeting Visualizations for Surgical Navigation of Iliosacral Screws.- Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.- A Radiomics-based Machine Learning Approach to Assess Collateral Circulation in Stroke on Non-contrast Computed Tomography.- Image-based Subthalamic Nucleus Segmentation for Deep Brain Surgery With Electrophysiology Aided Refinement.- 3D Slicer Craniomaxillofacial Modules Support Patient-specific Decision-making for Personalized Healthcare in Dental Research.- Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision.- Single-shot Deep Volumetric Regression for Mobile Medical Augmented Reality.- A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge.- Adversarial Prediction of Radiotherapy Treatment Machine Parameters.- ML-CDS 2020.- Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data.- Towards Automated Diagnosis with Attentive Multi-Modal Learning Using Electronic Health Records and Chest X-rays.- LUCAS: LUng CAncer Screening with Multimodal Biomarkers.- Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound.
Описание: The reader is guided through a development process and new opportunities for research and development are opened up: in modeling and validation of workflows, guided modeling, semi-automated modeling, advanced Bayesian networks, model-user interaction, inter-institutional modeling and quality management.
Описание: This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.
Описание: A compendium of health care quantitative techniques based in Excel Analytics and Decision Support in Health Care Operations is a comprehensive introductory guide to quantitative techniques, with practical Excel-based solutions for strategic health care management.
Описание: This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc.
Автор: Jain, Sarika Название: Understanding Semantics-Based Decision Support ISBN: 0367443139 ISBN-13(EAN): 9780367443139 Издательство: Taylor&Francis Рейтинг: Цена: 13473.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is an attempt to establish in the readers the importance of creating interoperable data stores and writing rules for handling this data. It also covers extracts from a few project dissertations and a research funded project that the author had supervised.
Описание: This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables.
Описание: The ability to differentiate patients who need emergent treatment from those who do not is a vital skill in Emergency Medicine. Solid history-taking, a thorough physical examination, appropriate use of laboratory tests and imaging, and sound clinical ju
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