Lin Yang – författare

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.

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.

 

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.

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.

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.

The book puts forward dynamically enabled cyber defense technology as a solution to the system homogenization problem. Based on the hierarchy of the protected information system entity, the book elaborates on current mainstream dynamic defense technologies from four aspects: the internal hardware platform, software service, information data and external network communication. It also ascertains their possible evolution routes, clarifies their relationship with existing security products, and makes macro analyses and discussions on security gain and overall system efficiency of these technologies.This book can be used as both a textbook for graduate courses related to electronic information as well as a reference for scientific researchers engaged in relevant research. It helps graduate students majoring in electronics and information sciences to gain an understanding in dynamically-enabled cyber defense. Scientists and engineers specialising in network security research should also find this book to be a useful guide on recent developments in network security.

This book is highly informative and carefully presented, providing scientific insights into the flood resources utilization in the Yangtze River Basin both for scholars and decision-makers. The book is for the purpose of analyzing the potential utilization of flood resources in the Yangtze River Basin and exploring effective ways to put forward the countermeasures against the risks. Major objectives of this book include: (1) revealing the characteristics of the inflow and the sediment variation in the upper reaches of the Yangtze River, quantitatively evaluating the potential utilization of the flood resources in the Yangtze River and demonstrating the feasibility of its utilization in the Basin; (2) proposing the necessity and feasibility of utilizing the flood resources by the Three Gorges Project; (3) shedding new light on the characteristics of the flood resources, presenting different methods of flood resources utilization in different regions over the Basin and raising the overall risk-optimized strategies of the flood resources utilization in the Yangtze River; (4) analyzing the risk of flood resources utilization for the Three Gorges Project regarding flood control, sediment, ecology, etc., and putting forward the risk-optimized countermeasures of flood resources utilization for the Three Gorges Project.

This book is highly informative and carefully presented, providing scientific insights into the flood resources utilization in the Yangtze River Basin both for scholars and decision-makers. The book is for the purpose of analyzing the potential utilization of flood resources in the Yangtze River Basin and exploring effective ways to put forward the countermeasures against the risks. Major objectives of this book include: (1) revealing the characteristics of the inflow and the sediment variation in the upper reaches of the Yangtze River, quantitatively evaluating the potential utilization of the flood resources in the Yangtze River and demonstrating the feasibility of its utilization in the Basin; (2) proposing the necessity and feasibility of utilizing the flood resources by the Three Gorges Project; (3) shedding new light on the characteristics of the flood resources, presenting different methods of flood resources utilization in different regions over the Basin and raising the overall risk-optimized strategies of the flood resources utilization in the Yangtze River; (4) analyzing the risk of flood resources utilization for the Three Gorges Project regarding flood control, sediment, ecology, etc., and putting forward the risk-optimized countermeasures of flood resources utilization for the Three Gorges Project.