Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics

Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics book. This book definitely worth reading, it is an incredibly well-written.

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

Le Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics Book PDF

Author : Le Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang
Publisher : Springer Nature
Page : 461 pages
File Size : 45,9 Mb
Release : 2019-09-19
Category : Computers
ISBN : 9783030139698

Get Book

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics by Le Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang Pdf

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 for Medical Image Analysis

S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Deep Learning for Medical Image Analysis Book PDF

Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Page : 544 pages
File Size : 47,8 Mb
Release : 2023-12-01
Category : Computers
ISBN : 9780323858885

Get Book

Deep Learning for Medical Image Analysis by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen Pdf

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest 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 cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Deep Learning and Convolutional Neural Networks for Medical Image Computing Book PDF

Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publisher : Springer
Page : 326 pages
File Size : 48,6 Mb
Release : 2017-07-12
Category : Computers
ISBN : 9783319429991

Get Book

Deep Learning and Convolutional Neural Networks for Medical Image Computing by Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang Pdf

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.

Introduction to Deep Learning for Healthcare

Cao Xiao,Jimeng Sun
Introduction to Deep Learning for Healthcare Book PDF

Author : Cao Xiao,Jimeng Sun
Publisher : Springer Nature
Page : 236 pages
File Size : 53,8 Mb
Release : 2021-11-11
Category : Medical
ISBN : 9783030821845

Get Book

Introduction to Deep Learning for Healthcare by Cao Xiao,Jimeng Sun Pdf

This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.

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

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

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
Page : 385 pages
File Size : 51,5 Mb
Release : 2017-09-07
Category : Computers
ISBN : 9783319675589

Get Book

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

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.

Convolutional Neural Networks for Medical Applications

Teik Toe Teoh
Convolutional Neural Networks for Medical Applications Book PDF

Author : Teik Toe Teoh
Publisher : Springer Nature
Page : 103 pages
File Size : 48,9 Mb
Release : 2023-03-23
Category : Computers
ISBN : 9789811988141

Get Book

Convolutional Neural Networks for Medical Applications by Teik Toe Teoh Pdf

Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models.

Deep Learning Applications in Medical Imaging

Saxena, Sanjay,Paul, Sudip
Deep Learning Applications in Medical Imaging Book PDF

Author : Saxena, Sanjay,Paul, Sudip
Publisher : IGI Global
Page : 274 pages
File Size : 40,9 Mb
Release : 2020-10-16
Category : Medical
ISBN : 9781799850724

Get Book

Deep Learning Applications in Medical Imaging by Saxena, Sanjay,Paul, Sudip Pdf

Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

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

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

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
Page : 401 pages
File Size : 50,7 Mb
Release : 2018-09-19
Category : Computers
ISBN : 9783030008895

Get Book

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

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 in Biomedical and Health Informatics

M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Deep Learning in Biomedical and Health Informatics Book PDF

Author : M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Publisher : CRC Press
Page : 251 pages
File Size : 54,6 Mb
Release : 2021-09-27
Category : Computers
ISBN : 9781000429121

Get Book

Deep Learning in Biomedical and Health Informatics by M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari Pdf

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Computational Analysis and Deep Learning for Medical Care

Amit Kumar Tyagi
Computational Analysis and Deep Learning for Medical Care Book PDF

Author : Amit Kumar Tyagi
Publisher : John Wiley & Sons
Page : 532 pages
File Size : 45,7 Mb
Release : 2021-08-10
Category : Computers
ISBN : 9781119785736

Get Book

Computational Analysis and Deep Learning for Medical Care by Amit Kumar Tyagi Pdf

The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book PDF

Author : E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publisher : CRC Press
Page : 344 pages
File Size : 45,8 Mb
Release : 2021-09-22
Category : Medical
ISBN : 9781000370454

Get Book

Handbook of Deep Learning in Biomedical Engineering and Health Informatics by E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi Pdf

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Ben Othman Soufiene,Chinmay Chakraborty
Machine Learning and Deep Learning Techniques for Medical Image Recognition Book PDF

Author : Ben Othman Soufiene,Chinmay Chakraborty
Publisher : CRC Press
Page : 270 pages
File Size : 41,5 Mb
Release : 2023-12-01
Category : Technology & Engineering
ISBN : 9781003805670

Get Book

Machine Learning and Deep Learning Techniques for Medical Image Recognition by Ben Othman Soufiene,Chinmay Chakraborty Pdf

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of 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 and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

Deep Learning and Data Labeling for Medical Applications

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
Deep Learning and Data Labeling for Medical Applications Book PDF

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
Page : 280 pages
File Size : 40,8 Mb
Release : 2016-10-07
Category : Computers
ISBN : 9783319469768

Get Book

Deep Learning and Data Labeling for Medical Applications by 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 Pdf

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.

Machine Learning in Medicine

Ayman El-Baz,Jasjit S. Suri
Machine Learning in Medicine Book PDF

Author : Ayman El-Baz,Jasjit S. Suri
Publisher : CRC Press
Page : 312 pages
File Size : 53,5 Mb
Release : 2021-08-04
Category : Computers
ISBN : 9781351588744

Get Book

Machine Learning in Medicine by Ayman El-Baz,Jasjit S. Suri Pdf

Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems. New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different activation functions for small- to medium-size biomedical datasets, detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments, dermatological machine learning clinical decision support systems, artificial intelligence-powered ultrasound for diagnosis, practical challenges with possible solutions for machine learning in medical imaging, epilepsy diagnosis from structural MRI, Alzheimer's disease diagnosis, classification of left ventricular hypertrophy, and intelligent medical language understanding. This book will help to advance scientific research within the broad field of machine learning in the medical field. It focuses on major trends and challenges in this area and presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.

Medical Imaging Informatics

Alex A.T. Bui,Ricky K. Taira
Medical Imaging Informatics Book PDF

Author : Alex A.T. Bui,Ricky K. Taira
Publisher : Springer Science & Business Media
Page : 454 pages
File Size : 53,5 Mb
Release : 2009-12-01
Category : Technology & Engineering
ISBN : 9781441903853

Get Book

Medical Imaging Informatics by Alex A.T. Bui,Ricky K. Taira Pdf

Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.