Domain Adaptation And Representation Transfer And Distributed And Collaborative Learning

Author : Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu
Publisher : Springer Nature
Page : 224 pages
File Size : 49,8 Mb
Release : 2020-09-25
Category : Computers
ISBN : 9783030605483

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Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning by Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu Pdf

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.

Author : Cristina Oyarzun Laura,M. Jorge Cardoso,Michal Rosen-Zvi,Georgios Kaissis,Marius George Linguraru,Raj Shekhar,Stefan Wesarg,Marius Erdt,Klaus Drechsler,Yufei Chen,Shadi Albarqouni,Spyridon Bakas,Bennett Landman,Nicola Rieke,Holger Roth,Xiaoxiao Li,Daguang Xu,Maria Gabrani,Ender Konukoglu,Michal Guindy,Daniel Rueckert,Alexander Ziller,Dmitrii Usynin,Jonathan Passerat-Palmbach
Publisher : Springer Nature
Page : 201 pages
File Size : 47,5 Mb
Release : 2021-11-13
Category : Computers
ISBN : 9783030908744

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Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning by Cristina Oyarzun Laura,M. Jorge Cardoso,Michal Rosen-Zvi,Georgios Kaissis,Marius George Linguraru,Raj Shekhar,Stefan Wesarg,Marius Erdt,Klaus Drechsler,Yufei Chen,Shadi Albarqouni,Spyridon Bakas,Bennett Landman,Nicola Rieke,Holger Roth,Xiaoxiao Li,Daguang Xu,Maria Gabrani,Ender Konukoglu,Michal Guindy,Daniel Rueckert,Alexander Ziller,Dmitrii Usynin,Jonathan Passerat-Palmbach Pdf

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.

Author : Shadi Albarqouni,Spyridon Bakas,Sophia Bano,M. Jorge Cardoso,Bishesh Khanal,Bennett Landman,Xiaoxiao Li,Chen Qin,Islem Rekik,Nicola Rieke,Holger Roth,Debdoot Sheet,Daguang Xu
Publisher : Springer Nature
Page : 215 pages
File Size : 53,5 Mb
Release : 2022-10-08
Category : Computers
ISBN : 9783031185236

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Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health by Shadi Albarqouni,Spyridon Bakas,Sophia Bano,M. Jorge Cardoso,Bishesh Khanal,Bennett Landman,Xiaoxiao Li,Chen Qin,Islem Rekik,Nicola Rieke,Holger Roth,Debdoot Sheet,Daguang Xu Pdf

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.

Author : Ninon Burgos,David Svoboda
Publisher : Academic Press
Page : 676 pages
File Size : 46,6 Mb
Release : 2022-06-18
Category : Computers
ISBN : 9780128243503

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Biomedical Image Synthesis and Simulation by Ninon Burgos,David Svoboda Pdf

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

Author : M. Hadi Amini
Publisher : Springer Nature
Page : 163 pages
File Size : 43,9 Mb
Release : 2024-06-23
Category : Electronic
ISBN : 9783031575679

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Distributed Machine Learning and Computing by M. Hadi Amini Pdf

Author : Hien Van Nguyen,Ronald Summers,Rama Chellappa
Publisher : Academic Press
Page : 430 pages
File Size : 46,7 Mb
Release : 2022-09-24
Category : Computers
ISBN : 9780323998529

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Meta Learning With Medical Imaging and Health Informatics Applications by Hien Van Nguyen,Ronald Summers,Rama Chellappa Pdf

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

Author : Ruidan Su,Yudong Zhang,Han Liu,Alejandro F Frangi
Publisher : Springer Nature
Page : 567 pages
File Size : 48,7 Mb
Release : 2024-01-20
Category : Technology & Engineering
ISBN : 9789811667756

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Medical Imaging and Computer-Aided Diagnosis by Ruidan Su,Yudong Zhang,Han Liu,Alejandro F Frangi Pdf

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.

Author : Amit Kumar Singh,Stefano Berretti
Publisher : Elsevier
Page : 444 pages
File Size : 51,9 Mb
Release : 2024-03-29
Category : Computers
ISBN : 9780443132346

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Data Fusion Techniques and Applications for Smart Healthcare by Amit Kumar Singh,Stefano Berretti Pdf

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

Author : Pronaya Bhattacharya,Ashwin Verma,Sudeep Tanwar
Publisher : CRC Press
Page : 308 pages
File Size : 43,5 Mb
Release : 2023-06-16
Category : Computers
ISBN : 9781000891317

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Federated Learning for Internet of Medical Things by Pronaya Bhattacharya,Ashwin Verma,Sudeep Tanwar Pdf

This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning. The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.

Author : Arnab Bhattacharya,Janice Lee Mong Li,Divyakant Agrawal,P. Krishna Reddy,Mukesh Mohania,Anirban Mondal,Vikram Goyal,Rage Uday Kiran
Publisher : Springer Nature
Page : 744 pages
File Size : 50,7 Mb
Release : 2022-04-26
Category : Computers
ISBN : 9783031001260

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Database Systems for Advanced Applications by Arnab Bhattacharya,Janice Lee Mong Li,Divyakant Agrawal,P. Krishna Reddy,Mukesh Mohania,Anirban Mondal,Vikram Goyal,Rage Uday Kiran Pdf

The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

Author : Gilmer Valdes,Lei Xing
Publisher : CRC Press
Page : 201 pages
File Size : 45,5 Mb
Release : 2023-08-14
Category : Computers
ISBN : 9781000903812

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Artificial Intelligence in Radiation Oncology and Biomedical Physics by Gilmer Valdes,Lei Xing Pdf

This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.

Author : Agbotiname Lucky Imoize,Mohammad S Obaidat,Houbing Herbert Song
Publisher : Elsevier
Page : 459 pages
File Size : 50,5 Mb
Release : 2024-06-10
Category : Computers
ISBN : 9780443138966

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Federated Learning for Digital Healthcare Systems by Agbotiname Lucky Imoize,Mohammad S Obaidat,Houbing Herbert Song Pdf

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

Author : Khang, Alex
Publisher : IGI Global
Page : 393 pages
File Size : 40,9 Mb
Release : 2024-02-09
Category : Medical
ISBN : 9798369332191

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AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications by Khang, Alex Pdf

Within the healthcare sector, a pressing need for transformative changes is growing. From chronic diseases to complex diagnostic procedures, the industry stands at the crossroads of technological innovation and a burgeoning demand for more efficient, precise interventions. Patient expectations are soaring, and the deluge of medical data is overwhelming traditional healthcare systems. It is within this challenging environment that AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications emerges as a beacon of insight and practical solutions. The traditional healthcare framework is struggling to keep pace with the diverse demands of patients and the ever-expanding volume of medical data. As diseases become more intricate, attempts to provide timely identification and precise treatment of ailments become increasingly elusive. The urgency for a paradigm shift in healthcare delivery is emphasized by the critical need for early interventions, particularly in disease prediction. This challenge necessitates a holistic approach that harnesses the power of artificial intelligence (AI) and innovative technologies to steer healthcare toward a more responsive and patient-centric future.

Author : Frank H. Miller
Publisher : Elsevier Health Sciences
Page : 255 pages
File Size : 43,5 Mb
Release : 2023-08-01
Category : Medical
ISBN : 9780443182891

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Advances in Clinical Radiology, 2023 E-Book by Frank H. Miller Pdf

Advances in Clinical Radiology reviews the year’s most important findings and updates within the field in order to provide radiologists with the current clinical information they need to improve patient outcomes. A distinguished editorial board, led by Dr. Frank H. Miller, identifies key areas of major progress and controversy and invites preeminent specialists to contribute original articles devoted to these topics. These insightful overviews in clinical radiology inform and enhance clinical practice by bringing concepts to a clinical level and exploring their everyday impact on patient care. Contains 20 articles on such topics as artificial intelligence and imaging of the liver; lung cancer screening update; musculoskeletal applications of cone-beam computed tomography; contrast-enhanced ultrasound; advances in imaging for headache and sinus disease; and more. Provides in-depth, clinical reviews in clinical radiology, providing actionable insights for clinical practice. Presents the latest information in the field under the leadership of an experienced editorial team. Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.

Author : Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert
Publisher : Springer Nature
Page : 676 pages
File Size : 49,7 Mb
Release : 2021-09-23
Category : Computers
ISBN : 9783030871994

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 by Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert Pdf

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.