Federated Learning In Medical Image Analysis
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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 Medical Imaging
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Author : Xiaoxiao Li
language : en
Publisher: Academic Press
Release Date : 2024-12-01
Federated Learning For Medical Imaging written by Xiaoxiao Li and has been published by Academic Press 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.
Deep Learning For Medical Image Analysis
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Author : S. Kevin Zhou
language : en
Publisher: Academic Press
Release Date : 2023-12-01
Deep Learning For Medical Image Analysis written by S. Kevin Zhou and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-01 with Computers categories.
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
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.
Advances In Deep Learning For Medical Image Analysis
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Author : Archana Mire
language : en
Publisher: CRC Press
Release Date : 2022-04-28
Advances In Deep Learning For Medical Image Analysis written by Archana Mire and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-28 with Technology & Engineering categories.
This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.
Medical Image Analysis
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Author : Alejandro Frangi
language : en
Publisher: Academic Press
Release Date : 2023-09-20
Medical Image Analysis written by Alejandro Frangi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-20 with Technology & Engineering categories.
Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing
Image Analysis And Processing Iciap 2023 Workshops
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Author : Gian Luca Foresti
language : en
Publisher: Springer Nature
Release Date : 2024-01-20
Image Analysis And Processing Iciap 2023 Workshops written by Gian Luca Foresti and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-20 with Computers categories.
The two-volume set LNCS 14365 and 14366 constitutes the papers of workshops hosted by the 22nd International Conference on Image Analysis and Processing, ICIAP 2023, held in Udine, Italy, in September 2023. In total, 72 workshop papers and 10 industrial poster session papers have been accepted for publication. Part II of the set, volume 14366, contains 41 papers from the following workshops:– Medical Imaging Hub:• Artificial Intelligence and Radiomics in Computer-Aided Diagnosis (AIR-CAD)• Multi-Modal Medical Imaging Processing (M3IP)• Federated Learning in Medical Imaging and Vision (FedMed)– Digital Humanities Hub:• Artificial Intelligence for Digital Humanities (AI4DH)• Fine Art Pattern Extraction and Recognition (FAPER)• Pattern Recognition for Cultural Heritage (PatReCH)• Visual Processing of Digital Manuscripts: Workflows, Pipelines, BestPractices (ViDiScript)
Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing
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Author : Rohit Raja
language : en
Publisher: CRC Press
Release Date : 2020-12-22
Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing written by Rohit Raja and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-22 with Medical categories.
Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field
Federated Learning And Privacy Preserving In Healthcare Ai
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-02
Federated Learning And Privacy Preserving In Healthcare Ai written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Medical categories.
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.