Download or read book From Prototype to Clinical Workflow Moving Machine Learning for Lesion Quantification into Neuroradiological Practice written by Raphael Meier and published by Frontiers Media SA. This book was released on 2022-08-02 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Other Artificial Intelligence Applications An Issue of Neuroimaging Clinics of North America E Book written by Reza Forghani and published by Elsevier Health Sciences. This book was released on 2020-10-23 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This issue of Neuroimaging Clinics of North America focuses on Artificial Intelligence and Machine Learning and is edited by Dr. Reza Forghani. Articles will include: A Brief History of Artificial Intelligence; Evolution of Approaches for Computerized Image Analysis; Overview of Machine Learning Part 1: Classic Approaches; Overview of Machine Learning Part 2: Artificial Neural Networks & Deep Learning; Overview of Natural Language Processing; Artificial Intelligence & Stroke Imaging: An East Coast Perspective; Artificial Intelligence & Stroke Imaging: A West Coast Perspective; Artificial Intelligence Applications for Brain Tumor Imaging; Diverse Applications of Artificial Intelligence in Neuroradiology; Artificial Intelligence Applications for Head and Neck Imaging; Artificial Intelligence Applications for Predictive Analytics and Workflow Optimization; Artificial Intelligence, Advanced Visualization, and 3D Printing; Ethical & Legal Considerations for Artificial Intelligence; Comprehensive (or 360) Artificial Intelligence: Beyond Image Interpretation Alone, and more!

Download or read book Machine Learning in Clinical Neuroimaging written by Ahmed Abdulkadir and published by Springer Nature. This book was released on 2022-10-07 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions. The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration.

Download or read book Artificial Intelligence for Medical Image Analysis of NeuroImaging Data written by Nianyin Zeng and published by Frontiers Media SA. This book was released on 2020-07-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Medical Imaging written by Guorong Wu and published by Academic Press. This book was released on 2016-08-11 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Neuroimaging Techniques in Clinical Practice written by Manoj Mannil and published by Springer Nature. This book was released on 2020-08-11 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise overview of emerging technologies in the field of modern neuroimaging. Fundamental principles of the main imaging modalities are described as well as advanced imaging techniqes including diffusion weighted imaging, perfusion imaging, arterial spin labeling, diffusion tensor imaging, intravoxel incoherent motion, MR spectroscopy, functional MRI, and artificial intelligence. The physical concepts underlying each imaging technique are carefully and clearly explained in a way suited to a medical audience without prior technical knowledge. In addition, the clinical applications of the various techniques are described with the aid of illustrative clinical examples. Helpful background information is also presented on the core principles of MRI and the evolution of neuroimaging, and important references to current medical research are highlighted. The book will meet the needs of a range of non-technological professionals with an interest in advanced neuroimaging, including radiology researchers and clinicians in the fields of neurology, neurosurgery, and psychiatry.

Download or read book Machine Learning in Medical Imaging written by Xiaohuan Cao and published by Springer Nature. This book was released on 2023-10-14 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning in Medical Imaging written by Xiaohuan Cao and published by Springer Nature. This book was released on 2023-10-14 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Driven Clinical Decision Making Using Deep Learning in Imaging written by M. F. Mridha and published by Springer Nature. This book was released on with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning in Clinical Neuroimaging written by Ahmed Abdulkadir and published by Springer Nature. This book was released on 2021-09-22 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series.

Download or read book Machine Learning and Deep Learning in Neuroimaging Data Analysis written by Anitha S. Pillai and published by CRC Press. This book was released on 2024-02-15 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Download or read book Machine and Deep Learning in Oncology Medical Physics and Radiology written by Issam El Naqa and published by Springer Nature. This book was released on 2022-02-02 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Download or read book Machine Learning in Clinical Neuroimaging written by Ahmed Abdulkadir and published by Springer Nature. This book was released on 2023-10-07 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings on Machine Learning and Clinical Applications.

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2017-01-18 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning in Medical Imaging written by Chunfeng Lian and published by Springer Nature. This book was released on 2021-09-25 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Deep Learning for Medical Image Analysis written by Archana Mire and published by CRC Press. This book was released on 2022-04-26 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Understanding and Interpreting Machine Learning in Medical Image Computing Applications written by Danail Stoyanov and published by Springer. This book was released on 2018-10-23 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.