Domain Adaptation And Representation Transfer And Distributed And Collaborative Learning
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Domain Adaptation And Representation Transfer And Distributed And Collaborative Learning
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Author : Shadi Albarqouni
language : en
Publisher: Springer Nature
Release Date : 2020-09-25
Domain Adaptation And Representation Transfer And Distributed And Collaborative Learning written by Shadi Albarqouni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Computers categories.
This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.
Clinical Image Based Procedures Distributed And Collaborative Learning Artificial Intelligence For Combating Covid 19 And Secure And Privacy Preserving Machine Learning
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Author : Cristina Oyarzun Laura
language : en
Publisher: Springer Nature
Release Date : 2021-11-13
Clinical Image Based Procedures Distributed And Collaborative Learning Artificial Intelligence For Combating Covid 19 And Secure And Privacy Preserving Machine Learning written by Cristina Oyarzun Laura and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-13 with Computers categories.
This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community.
Distributed Machine Learning And Computing
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Author : M. Hadi Amini
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
Distributed Machine Learning And Computing written by M. Hadi Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Technology & Engineering categories.
This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques.
Dynamics Of Disasters
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Author : Ilias S. Kotsireas
language : en
Publisher: Springer Nature
Release Date : 2024-12-23
Dynamics Of Disasters written by Ilias S. Kotsireas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-23 with Mathematics categories.
Based on the “Sixth International Conference on Dynamics of Disasters” (Piraeus, Greece, July 2023), this volume includes contributions from experts who share their latest discoveries on disasters either caused by natural phenomena or human activities. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery and uses for mathematical models to describe disasters and their impacts. This volume includes additional invited manuscripts from other experts and leaders in the field. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies and will engage readers from a wide variety of fields and backgrounds.
Meta Learning With Medical Imaging And Health Informatics Applications
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Author : Hien Van Nguyen
language : en
Publisher: Academic Press
Release Date : 2022-09-24
Meta Learning With Medical Imaging And Health Informatics Applications written by Hien Van Nguyen and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-24 with Computers categories.
Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks' fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. - First book on applying Meta Learning to medical imaging - Pioneers in the field as contributing authors to explain the theory and its development - Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly
Distributed Collaborative And Federated Learning And Affordable Ai And Healthcare For Resource Diverse Global Health
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Author : Shadi Albarqouni
language : en
Publisher: Springer Nature
Release Date : 2022-10-08
Distributed Collaborative And Federated Learning And Affordable Ai And Healthcare For Resource Diverse Global Health written by Shadi Albarqouni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-08 with Computers categories.
This book constitutes the refereed proceedings of the Third MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event. DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusing on the comparison, evaluation, and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases or where information privacy is a priority. For FAIR 2022, 4 papers from 9 submissions were accepted for publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting.
Biomedical Image Synthesis And Simulation
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Author : Ninon Burgos
language : en
Publisher: Academic Press
Release Date : 2022-06-18
Biomedical Image Synthesis And Simulation written by Ninon Burgos and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-18 with Computers categories.
Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future. - Gives state-of-the-art methods in (bio)medical image synthesis - Explains the principles (background) of image synthesis methods - Presents the main applications of biomedical image synthesis methods
Medical Imaging And Computer Aided Diagnosis
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Author : Ruidan Su
language : en
Publisher: Springer Nature
Release Date : 2023-12-19
Medical Imaging And Computer Aided Diagnosis written by Ruidan Su and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Technology & Engineering categories.
This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation.
Federated Learning For Neural Disorders In Healthcare 6 0
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Author : Kishor Kumar Reddy C
language : en
Publisher: CRC Press
Release Date : 2025-05-14
Federated Learning For Neural Disorders In Healthcare 6 0 written by Kishor Kumar Reddy C and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-14 with Technology & Engineering categories.
This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders. This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.
Data Fusion Techniques And Applications For Smart Healthcare
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Author : Amit Kumar Singh
language : en
Publisher: Elsevier
Release Date : 2024-03-12
Data Fusion Techniques And Applications For Smart Healthcare written by Amit Kumar Singh and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-12 with Computers categories.
Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, x-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. These large and highly diverse amounts of information need to be organized and mined in an appropriate way so that meaningful information can be extracted. New multimodal data fusion techniques are able to combine salient information into one single source to ensure better diagnostic accuracy and assessment. Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. This book can be used as a reference for practicing engineers, scientists, and researchers. It will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications. - Presents broad coverage of applied case studies using data fusion techniques to mine, organize, and interpret medical data - Investigates how data fusion techniques offer a new solution for dealing with massive amounts of medical data coming from diverse sources and multiple formats - Focuses on identifying challenges, solutions, and new directions that will be useful for graduate students, researchers, and practitioners from government, academia, industry, and healthcare