Federated Learning For Medical Imaging
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Federated Learning For Medical Imaging
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Author : Xiaoxiao Li
language : en
Publisher: Elsevier
Release Date : 2024-12-01
Federated Learning For Medical Imaging written by Xiaoxiao Li and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-01 with Computers categories.
Federated Learning for Medical Imaging: Principles, Algorithms and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. In addition, it provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc. This is a complete resource for computer scientists and engineers as well as clinicians and medical care policymakers wanting to learn about the application of federated learning to medical imaging. Presents the specific challenges in developing and deploying FL to medical imaging Explains the tools for developing or using FL Provides state-of-the-art algorithms in the field with open source software on GitHub Gives insights into potential issues and solutions of building FL infrastructures for real-world applications Informs researchers on future research challenges of building real-world FL applications
Federated Learning Techniques And Its Application In The Healthcare Industry
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Author : H L Gururaj
language : en
Publisher: World Scientific
Release Date : 2024-05-28
Federated Learning Techniques And Its Application In The Healthcare Industry written by H L Gururaj and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Computers categories.
Federated Learning is currently an emerging technology in the field of machine learning. Federated Learning is a structure which trains a centralized model for a given assignment, where the data is de-centralized across different edge devices or servers. This enables preservation of the confidentiality of data on various edge devices, as only the updated outcomes of the models are shared with the centralized model. This means the data can remain on each edge device, while we can still train a model using that data.Federated Learning has greatly increased the potential to transmute data in the healthcare industry, enabling healthcare professionals to improve treatment of patients.This book comprises chapters on applying Federated models in the field of healthcare industry.Federated Learning mainly concentrates on securing the privacy of data by training local data in a shared global model without putting the training data in a centralized location. The importance of federated learning lies in its innumerable uses in health care that ranges from maintaining the privacy of raw data of the patients, discover clinically alike patients, forecasting hospitalization due to cardiac events impermanence and probable solutions to the same. The goal of this edited book is to provide a reference guide to the theme.
Federated Learning In Medical Image Analysis
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Author : Erfan Darzidehkalani
language : en
Publisher:
Release Date : 2024
Federated Learning In Medical Image Analysis written by Erfan Darzidehkalani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Federated Learning For Digital Healthcare Systems
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Author : Agbotiname Lucky Imoize
language : en
Publisher: Elsevier
Release Date : 2024-06-10
Federated Learning For Digital Healthcare Systems written by Agbotiname Lucky Imoize and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-10 with Computers categories.
Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry
Federated Learning And Ai For Healthcare 5 0
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Author : Hassan, Ahdi
language : en
Publisher: IGI Global
Release Date : 2023-12-18
Federated Learning And Ai For Healthcare 5 0 written by Hassan, Ahdi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-18 with Medical categories.
The Healthcare sector is evolving with Healthcare 5.0, promising better patient care and efficiency. However, challenges like data security and analysis arise due to increased digitization. Federated Learning and AI for Healthcare 5.0 offers solutions, explaining cloud computing's role in managing data and advocating for security measures. It explores federated learning's use in maintaining data privacy during analysis, presenting practical cases for implementation. The book also addresses emerging tech like quantum computing and blockchain-based services, envisioning an innovative Healthcare 5.0. It empowers healthcare professionals, IT experts, and data scientists to leverage these technologies for improved patient care and system efficiency, making Healthcare 5.0 secure and patient centric.
Federated Deep Learning For Healthcare
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Author : Amandeep Kaur
language : en
Publisher: CRC Press
Release Date : 2024-10-02
Federated Deep Learning For Healthcare written by Amandeep Kaur and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Computers categories.
This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information. Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.
Machine Learning In Medical Imaging
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Author : Xiaohuan Cao
language : en
Publisher: Springer Nature
Release Date : 2023-10-14
Machine Learning In Medical Imaging written by Xiaohuan Cao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-14 with Computers categories.
The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
Machine Learning In Medical Imaging And Computer Vision
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Author : Amita Nandal
language : en
Publisher: IET
Release Date : 2024-01-30
Machine Learning In Medical Imaging And Computer Vision written by Amita Nandal and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.
This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses.
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 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.