Machine Learning And Medical Imaging
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Machine Learning And Medical Imaging
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Author : Guorong Wu
language : en
Publisher: Academic Press
Release Date : 2016-08-11
Machine Learning And Medical Imaging written by Guorong Wu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-11 with Computers categories.
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques
Machine Learning In Medical Imaging
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Author : Chunfeng Lian
language : en
Publisher: Springer Nature
Release Date : 2021-09-25
Machine Learning In Medical Imaging written by Chunfeng Lian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-25 with Computers categories.
This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually.
Deep Learning For Medical Image Analysis
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Author : S. Kevin Zhou
language : en
Publisher: Academic Press
Release Date : 2017-01-18
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 2017-01-18 with Computers categories.
Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache
Deep Learning Applications In Medical Imaging
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Author : Saxena, Sanjay
language : en
Publisher: IGI Global
Release Date : 2020-10-16
Deep Learning Applications In Medical Imaging written by Saxena, Sanjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Medical categories.
Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.
Artificial Intelligence In Medical Imaging
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Author : Erik R. Ranschaert
language : en
Publisher: Springer
Release Date : 2019-01-29
Artificial Intelligence In Medical Imaging written by Erik R. Ranschaert and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with Medical categories.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implicationsfor radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Deep Learning Models For Medical Imaging
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Author : KC Santosh
language : en
Publisher: Academic Press
Release Date : 2021-09-07
Deep Learning Models For Medical Imaging written by KC Santosh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-07 with Computers categories.
Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. - Provides a step-by-step approach to develop deep learning models - Presents case studies showing end-to-end implementation (source codes: available upon request)
Artificial Intelligence In Medical Imaging
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Author : Lia Morra
language : en
Publisher: CRC Press
Release Date : 2019-11-25
Artificial Intelligence In Medical Imaging written by Lia Morra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-25 with Science categories.
Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective
Medical Image Recognition Segmentation And Parsing
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Author : S. Kevin Zhou
language : en
Publisher: Academic Press
Release Date : 2015-12-11
Medical Image Recognition Segmentation And Parsing 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 2015-12-11 with Computers categories.
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: - Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects - Methods and theories for medical image recognition, segmentation and parsing of multiple objects - Efficient and effective machine learning solutions based on big datasets - Selected applications of medical image parsing using proven algorithms - Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects - Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets - Includes algorithms for recognizing and parsing of known anatomies for practical applications
Machine Learning For Medical Image Reconstruction
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Author : Nandinee Haq
language : en
Publisher: Springer Nature
Release Date : 2021-09-29
Machine Learning For Medical Image Reconstruction written by Nandinee Haq and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-29 with Computers categories.
This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Understanding And Interpreting Machine Learning In Medical Image Computing Applications
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Author : Danail Stoyanov
language : en
Publisher: Springer
Release Date : 2018-10-23
Understanding And Interpreting Machine Learning In Medical Image Computing Applications written by Danail Stoyanov and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-23 with Computers categories.
This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.