December 4, 2020

Download Ebook Free Deep Learning For Medical Image Analysis

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Release Date : 2017-01-18
Category : Computers
Total pages :458
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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

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
Author : Gobert Lee,Hiroshi Fujita
Publisher : Springer Nature
Release Date : 2020-02-06
Category : Medical
Total pages :181
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This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
Publisher : Academic Press
Release Date : 2016-08-11
Category : Technology & Engineering
Total pages :512
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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

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

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
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
Release Date : 2017-09-07
Category : Computers
Total pages :385
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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.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing
Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publisher : Springer
Release Date : 2017-07-12
Category : Computers
Total pages :326
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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 Healthcare

Deep Learning in Healthcare
Author : Yen-Wei Chen,Lakhmi C. Jain
Publisher : Springer Nature
Release Date : 2019-11-18
Category : Technology & Engineering
Total pages :218
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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.

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

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
Author : Le Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang
Publisher : Springer
Release Date : 2020-10-01
Category : Computers
Total pages :461
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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 book’s 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.

Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging
Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
Publisher : Academic Press
Release Date : 2021-01-01
Category : Computers
Total pages :180
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Deep Learning Models for Medical Imaging is suitable for computer science, medical imaging and biomedical engineering researchers and students who need up-to-date deep learning tools to apply to medical image analysis problems. The book presents deep learning concepts and modeling as applied to medical imaging and/or healthcare, using two different real-world case studies, providing complete implementation (via GitHub) of both standard (e.g. LeNet, Alexnet, VGGNet, ResNet and InceptionNet) and recent models (Mobile net and squeeze-and excitation net). Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation Includes codes provided in GitHub

Medical Image Recognition, Segmentation and Parsing

Medical Image Recognition, Segmentation and Parsing
Author : S. Kevin Zhou
Publisher : Academic Press
Release Date : 2015-12-11
Category : Computers
Total pages :542
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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

Medical Imaging

Medical Imaging
Author : K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publisher : CRC Press
Release Date : 2019-08-20
Category : Computers
Total pages :238
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The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
Author : Nilanjan Dey
Publisher : Academic Press
Release Date : 2019-07-31
Category : Science
Total pages :218
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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications Introduces several techniques for medical image processing and analysis for CAD systems design

Deep Learning and Data Labeling for Medical Applications

Deep Learning and Data Labeling for Medical Applications
Author : Gustavo Carneiro,Diana Mateus,Loïc Peter,Andrew Bradley,João Manuel R. S. Tavares,Vasileios Belagiannis,João Paulo Papa,Jacinto C. Nascimento,Marco Loog,Zhi Lu,Jaime S. Cardoso,Julien Cornebise
Publisher : Springer
Release Date : 2016-10-07
Category : Computers
Total pages :280
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This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

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

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
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
Release Date : 2018-09-19
Category : Computers
Total pages :387
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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.

Deep Learning Methods for Medical Image Computing

Deep Learning Methods for Medical Image Computing
Author : Haofu Liao
Publisher : Unknown
Release Date : 2020
Category :
Total pages :206
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"A long-standing goal of the medical community is to present and analyze medical images efficiently and intelligently. On the one hand, it means to find efficient ways to acquire high-quality medical images that can readily be used by healthcare providers. On the other hand, it means to discover intelligent ways to interpret medical images to facilitate the healthcare delivery. To this end, researchers and medical professionals usually seek to use computerized systems that are empowered by machine learning techniques for the processing of medical images. A pivotal step in applying machine learning is to obtain informative representations that well describe medical images. Conventionally, this is performed with manual feature engineering which however requires considerable domain expertise in medicine. A possible workaround is to allow the model to automatically discover latent representations about the target domain from raw data. To this end, this thesis focuses on deep learning which is only a subset of the broader family of machine learning, but has recently made unprecedented progress and exhibits incredible ability in discovering intricate structures from high dimensional data. For many computer vision tasks, deep learning approaches have achieved state-of-the-art performance by a significant margin. This thesis develops deep learning models and techniques for medical image analysis, reconstruction and synthesis. In medical image analysis, we concentrate on understanding the content of the medical images and giving guidance to medical practitioners. In particular, we investigate deep learning ways to address classification, detection, segmentation and registration of medical images. In medical image reconstruction and synthesis, we propose to use deep learning ways to inherently learn the medical data space and effectively synthesize realistic medical images. For the reconstruction, we aim to generate high-quality medical images with fewer artifacts. For the synthesis, our goal is to generate realistic medical images to help the learning of medical image analysis or reconstruction models. The contribution of this thesis work is threefold. First, we propose a variety of approaches in leveraging deep learning to solve problems in medicine. Second, we show the importance and effectiveness of medical knowledge fusion in the design of deep learning architectures. Third, we show the potential of deep generative models in addressing medical image reconstruction and synthesis problems"--Pages xvii-xviii.

Advanced Machine Vision Paradigms for Medical Image Analysis

Advanced Machine Vision Paradigms for Medical Image Analysis
Author : Tapan K. Gandhi,Siddhartha Bhattacharyya,Sourav De,Debanjan Konar,Sandip Dey
Publisher : Academic Press
Release Date : 2020-08-11
Category : Computers
Total pages :308
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Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence Highlights the advancement of conventional approaches in the field of medical image processing Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis