Deep Learning for Medical Image Analysis

Author: S. Kevin Zhou,Hayit Greenspan,Dinggang Shen

Publisher: Academic Press

ISBN: 0128104090

Category: Technology & Engineering

Page: 458

View: 2851

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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
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Deep Learning in Healthcare

Paradigms and Applications

Author: Yen-Wei Chen,Lakhmi C. Jain

Publisher: Springer Nature

ISBN: 3030326063

Category: Technology & Engineering

Page: 218

View: 4363

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This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.
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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings

Author: M. Jorge Cardoso,Tal Arbel,Gustavo Carneiro,Tanveer Syeda-Mahmood,João Manuel R.S. Tavares,Mehdi Moradi,Andrew Bradley,Hayit Greenspan,João Paulo Papa,Anant Madabhushi,Jacinto C. Nascimento,Jaime S. Cardoso,Vasileios Belagiannis,Zhi Lu

Publisher: Springer

ISBN: 3319675583

Category: Computers

Page: 385

View: 5540

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This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. 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.
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Machine Learning and Medical Imaging

Author: Guorong Wu,Dinggang Shen,Mert Sabuncu

Publisher: Academic Press

ISBN: 0128041145

Category: Technology & Engineering

Page: 512

View: 1307

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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
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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

Author: Danail Stoyanov,Zeike Taylor,Gustavo Carneiro,Tanveer Syeda-Mahmood,Anne Martel,Lena Maier-Hein,João Manuel R.S. Tavares,Andrew Bradley,João Paulo Papa,Vasileios Belagiannis,Jacinto C. Nascimento,Zhi Lu,Sailesh Conjeti,Mehdi Moradi,Hayit Greenspan,Anant Madabhushi

Publisher: Springer

ISBN: 3030008894

Category: Computers

Page: 387

View: 9135

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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.
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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Author: Le Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang

Publisher: N.A

ISBN: 9783030139704

Category: Artificial intelligence

Page: 461

View: 2499

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This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The books chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
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Deep Learning Techniques for Biomedical and Health Informatics

Author: Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma

Publisher: Academic Press

ISBN: 0128190620

Category: Science

Page: 367

View: 9363

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Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis
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Handbook of Medical Image Computing and Computer Assisted Intervention

Author: S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger

Publisher: Academic Press

ISBN: 0128165863

Category: Computers

Page: 1072

View: 9640

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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
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