Marginal Space Learning For Medical Image Analysis

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Marginal Space Learning For Medical Image Analysis

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Author : Yefeng Zheng
language : en
Publisher: Springer Science & Business
Release Date : 2014-04-16

Marginal Space Learning For Medical Image Analysis written by Yefeng Zheng and has been published by Springer Science & Business this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-16 with Computers categories.

Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications. This book presents an efficient object detection and segmentation framework, called Marginal Space Learning, which runs at a sub-second speed on a current desktop computer, faster than the state-of-the-art. Trained with a sufficient number of data sets, Marginal Space Learning is also robust under imaging artifacts, noise and anatomical variations. The book showcases 35 clinical applications of Marginal Space Learning and its extensions to detecting and segmenting various anatomical structures, such as the heart, liver, lymph nodes and prostate in major medical imaging modalities (CT, MRI, X-Ray and Ultrasound), demonstrating its efficiency and robustness.

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

Proceedings Of The Second International Conference On Information Management And Machine Intelligence

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Author : Dinesh Goyal
language : en
Publisher: Springer Nature
Release Date : 2021-01-22

Proceedings Of The Second International Conference On Information Management And Machine Intelligence written by Dinesh Goyal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-22 with Technology & Engineering categories.

This book features selected papers presented at Second International Conference on International Conference on Information Management & Machine Intelligence (ICIMMI 2020) held at Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India during 24 – 25 July 2020. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.

Deep Learning And Convolutional Neural Networks For Medical Image Computing

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Author : Le Lu
language : en
Publisher: Springer
Release Date : 2017-07-12

Deep Learning And Convolutional Neural Networks For Medical Image Computing written by Le Lu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Computers categories.

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Deep Learning In Medical Image Analysis And Multimodal Learning For Clinical Decision Support

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Author : Danail Stoyanov
language : en
Publisher: Springer
Release Date : 2018-09-19

Deep Learning In Medical Image Analysis And Multimodal Learning For Clinical Decision Support 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 2018-09-19 with Computers categories.

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 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 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Advances In Computational Techniques For Biomedical Image Analysis

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Author : Deepika Koundal
language : en
Publisher: Academic Press
Release Date : 2020-05-28

Advances In Computational Techniques For Biomedical Image Analysis written by Deepika Koundal and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-28 with Computers categories.

Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation. Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications is ideal for researchers and post graduate students developing systems and tools for health-care systems. - Covers various challenges and common research issues related to biomedical image analysis - Describes advanced computational approaches for biomedical image analysis - Shows how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. - Explores a range of computational algorithms and techniques, such as neural networks, fuzzy sets, and evolutionary optimization - Explores cloud based medical imaging together with medical imaging security and forensics

Medical Imaging And Augmented Reality

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Author : Hongen Liao
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09

Medical Imaging And Augmented Reality written by Hongen Liao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09 with Computers categories.

This book constitutes the refereed proceedings of the 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010, held in Beijing, China, in September 2010. The 60 revised full papers presented were carefully reviewed and selected from 139 submissions. The papers are organized in topical sections on image segmentation, image registration, shape modeling and morphometry, image analysis, diffusion tensor image, computer assisted intervention, medical image computing, visualization and application, segmentation and classification, medical image understanding, image-guided surgery, and augmented reality.

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

Handbook Of Medical Image Computing And Computer Assisted Intervention

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Author : S. Kevin Zhou
language : en
Publisher: Academic Press
Release Date : 2019-10-18

Handbook Of Medical Image Computing And Computer Assisted Intervention 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 2019-10-18 with Computers categories.

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. - Presents the key research challenges in medical image computing and computer-assisted intervention - Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society - Contains state-of-the-art technical approaches to key challenges - Demonstrates proven algorithms for a whole range of essential medical imaging applications - Includes source codes for use in a plug-and-play manner - Embraces future directions in the fields of medical image computing and computer-assisted intervention

Functional Imaging And Modeling Of The Heart

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Author : Nicholas Ayache
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-05-25

Functional Imaging And Modeling Of The Heart written by Nicholas Ayache and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-25 with Computers categories.

This book constitutes the refereed proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2009, held in Nice, France in June 2009. The 54 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions cover topics such as cardiac imaging and electrophysiology, cardiac architecture imaging and analysis, cardiac imaging, cardiac electrophysiology, cardiac motion estimation, cardiac mechanics, cardiac image analysis, cardiac biophysical simulation, cardiac research platforms, and cardiac anatomical and functional imaging.