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Author: Arun Solanki
Publisher: Springer Nature
ISBN: 3031432053
Category : Medical
Languages : en
Pages : 255
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Book Description
Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue. Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. This book covers the application of GANs on medical imaging augmentation and segmentation.
Author: Arun Solanki
Publisher: Springer Nature
ISBN: 3031432053
Category : Medical
Languages : en
Pages : 255
Get Book Here
Book Description
Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue. Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. This book covers the application of GANs on medical imaging augmentation and segmentation.
Author: Fabrice Dedouit
Publisher: Springer Nature
ISBN: 3030833526
Category : Medical
Languages : en
Pages : 204
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Book Description
This superbly illustrated book examines all aspects of the use of modern post-mortem imaging in forensic investigations, which has flourished since the introduction of multidetector computed tomography and magnetic resonance imaging. Readers will find guidance on the applications of all relevant imaging modalities and contrast media. Analogies and differences between forensic and clinical imaging are highlighted, and it is explained what lessons forensic imaging holds for clinical radiology, and vice versa. The remainder of the book comprehensively documents the typical “normal” post-mortem findings and the imaging presentations in various forms of trauma and nontraumatic forensic cases, including those in which medical liability may be an issue. The authors are radiologists and forensic radiologists from across the world who have extensive experience in post-mortem imaging. The book is primarily intended for forensic pathologists, radiologists, and radiographers seeking practical information on forensic imaging, but it will also be of interest to others, such as lawyers, who encounter this specialty during their professional activities.
Author: Alejandro Frangi
Publisher: Academic Press
ISBN: 0128136588
Category : Technology & Engineering
Languages : en
Pages : 700
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Book Description
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. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing
Author: Ignacio Rojas
Publisher: Springer Nature
ISBN: 3030453855
Category : Science
Languages : en
Pages : 843
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Book Description
This volume constitutes the proceedings of the 8th International Work-Conference on IWBBIO 2020, held in Granada, Spain, in May 2020. The total of 73papers presented in the proceedings, was carefully reviewed and selected from 241 submissions. The papers are organized in topical sections as follows: Biomarker Identification; Biomedical Engineering; Biomedical Signal Analysis; Bio-Nanotechnology; Computational Approaches for Drug Design and Personalized Medicine; Computational Proteomics and Protein-Protein Interactions; Data Mining from UV/VIS/NIR Imaging and Spectrophotometry; E-Health Technology, Services and Applications; Evolving Towards Digital Twins in Healthcare (EDITH); High Performance in Bioinformatics; High-Throughput Genomics: Bioinformatic Tools and Medical Applications; Machine Learning in Bioinformatics; Medical Image Processing; Simulation and Visualization of Biological Systems.
Author: Voulodimos, Athanasios
Publisher: IGI Global
ISBN: 1522552952
Category : Computers
Languages : en
Pages : 396
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Book Description
3D image reconstruction is used in many fields, such as medicine, entertainment, and computer science. This highly demanded process comes with many challenges, such as images becoming blurry by atmospheric turbulence, getting snowed with noise, or becoming damaged within foreign regions. It is imperative to remain well-informed with the latest research in this field. Recent Advances in 3D Imaging, Modeling, and Reconstruction is a collection of innovative research on the methods and common techniques of image reconstruction as well as the accuracy of these methods. Featuring coverage on a wide range of topics such as ray casting, holographic techniques, and machine learning, this publication is ideally designed for graphic designers, computer engineers, medical professionals, robotics engineers, city planners, game developers, researchers, academicians, and students.
Author: Hayit Greenspan
Publisher: Springer Nature
ISBN: 3030326896
Category : Computers
Languages : en
Pages : 202
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Book Description
This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Author: Singla, Babita
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 466
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Book Description
The healthcare sector is at a critical juncture, facing the pressing need to integrate generative AI technologies responsibly. Despite the promising benefits, such as improved diagnostics, personalized treatments, and streamlined operations, the adoption of AI in healthcare poses significant challenges. These challenges include ethical dilemmas, regulatory complexities, and the need for governance frameworks to ensure the technology's responsible use. Revolutionizing the Healthcare Sector with AI offers a comprehensive solution to these challenges. It provides a deep dive into the adoption, integration, scalability, and sustainability of generative AI in healthcare and a thorough analysis of governance, ethical, and regulatory issues. By offering insights from researchers, practitioners, patients, and policymakers, this book is a platform for responsible AI adoption in healthcare.
Author: G. Schreier
Publisher: IOS Press
ISBN: 1643680854
Category : Medical
Languages : en
Pages : 294
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Book Description
Successful digital healthcare depends on the effective flow of a complete chain of information; from the sensor, via multiple steps of processing, to the actuator, which can be anything from a human healthcare professional to a robot. Along this pathway, methods for automating the processing of information, like signal processing, machine learning, predictive analytics and decision support, play an increasing role in providing actionable information and supporting personalized and preventive healthcare concepts in both biomedical and digital healthcare systems and applications. ICT systems in healthcare and biomedical systems and devices are very closely related, and in the future they will become increasingly intertwined. Indeed, it is already often difficult to delineate where the one ends and the other begins. This book presents the intended proceedings of the dHealth 2020 annual conference on the general topic of health Informatics and digital health, which was due to be held in Vienna, Austria, on 19 and 20 May 2020, but which was cancelled due to the COVID-19 pandemic. The decision was nevertheless taken to publish these proceedings, which include the 40 papers which would have been delivered at the conference. The special topic for the 2020 edition of the conference was Biomedical Informatics for Health and Care. The book provides an overview of current developments in health informatics and digital health, and will be of interest to researchers and healthcare practitioners alike.
Author: Suman Kumar Swarnkar
Publisher: CRC Press
ISBN: 1040271235
Category : Technology & Engineering
Languages : en
Pages : 280
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Book Description
In the ever-evolving landscape of cancer treatment, the fusion of artificial intelligence (AI) with medical science marks a groundbreaking shift toward more precise, efficient, and personalized healthcare. Artificial Intelligence Revolutionizing Cancer Care: Precision Diagnosis and Patient-Centric Healthcare delves into the transformative power of AI, offering a comprehensive exploration of its role in enhancing cancer diagnosis, treatment, and patient management. This edited volume brings together leading experts and researchers who illuminate the latest advancements in AI technologies applied to oncology. From machine learning algorithms that predict cancer progression to sophisticated imaging techniques that improve diagnostic accuracy, this book covers a spectrum of innovations reshaping cancer care. Key highlights include precision diagnosis, uncovering how AI-driven tools are revolutionizing the early detection and accurate classification of various cancer types, leading to better patient outcomes; patient-centric approaches, exploring the shift toward personalized medicine, where AI tailors treatment protocols to individual patient profiles, ensuring more effective and targeted therapies; and ethical and practical considerations, gaining insights into the ethical, practical, and regulatory challenges of integrating AI in healthcare, emphasizing the need for patient privacy and data security. Additionally, the book looks ahead to the potential future applications of AI in oncology, including predictive analytics, robotic surgery, and beyond. Artificial Intelligence Revolutionizing Cancer Care is an essential resource for medical professionals, researchers, and students seeking to understand the intersection of AI and oncology. It offers a visionary perspective on how cutting-edge technology is poised to enhance patient care and transform the fight against cancer. This book focuses on the critical intersection of artificial intelligence and cancer diagnosis within the healthcare sector emphasizes the real-world impact of artificial intelligence in improving cancer detection, treatment, and overall patient care covers artificial intelligence algorithms, machine learning techniques, medical image analysis, predictive modeling, and patient care applications explores how artificial intelligence technologies enhance the patient’s experience, resulting in better outcomes and reduced healthcare disparities provides readers with an understanding of the mathematics underpinning machine learning models, including decision trees, support vector machines, and deep neural networks It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, and information technology.
Author: Shadi Albarqouni
Publisher: Springer Nature
ISBN: 3030877221
Category : Computers
Languages : en
Pages : 276
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Book Description
This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning.
