Convolutional Neural Networks For Medical Applications

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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 : 52,6 Mb
Release : 2023-03-23
Category : Computers
ISBN : 9789811988141

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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.

Convolutional Neural Networks for Medical Image Processing Applications

Saban Ozturk
Convolutional Neural Networks for Medical Image Processing Applications Book PDF

Author : Saban Ozturk
Publisher : CRC Press
Page : 294 pages
File Size : 40,7 Mb
Release : 2022-12-23
Category : Science
ISBN : 9781000818055

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Convolutional Neural Networks for Medical Image Processing Applications by Saban Ozturk Pdf

The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

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 : 49,6 Mb
Release : 2019-09-19
Category : Computers
ISBN : 9783030139698

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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 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 : 46,6 Mb
Release : 2017-07-12
Category : Computers
ISBN : 9783319429991

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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.

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 : 48,7 Mb
Release : 2016-10-07
Category : Computers
ISBN : 9783319469768

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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.

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Annamalai Suresh,R. Udendran,S. Vimal
Deep Neural Networks for Multimodal Imaging and Biomedical Applications Book PDF

Author : Annamalai Suresh,R. Udendran,S. Vimal
Publisher : Medical Information Science Reference
Page : 294 pages
File Size : 44,7 Mb
Release : 2020
Category : Computers
ISBN : 1799835936

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Deep Neural Networks for Multimodal Imaging and Biomedical Applications by Annamalai Suresh,R. Udendran,S. Vimal Pdf

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

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 : 45,5 Mb
Release : 2021-11-11
Category : Medical
ISBN : 9783030821845

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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.

Convolutional Neural Networks for Medical Image Processing Applications

Saban Ozturk
Convolutional Neural Networks for Medical Image Processing Applications Book PDF

Author : Saban Ozturk
Publisher : CRC Press
Page : 275 pages
File Size : 41,8 Mb
Release : 2022-12-23
Category : Science
ISBN : 9781000818024

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Convolutional Neural Networks for Medical Image Processing Applications by Saban Ozturk Pdf

The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

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 : 54,6 Mb
Release : 2023-12-01
Category : Computers
ISBN : 9780323858885

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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 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 : 40,7 Mb
Release : 2020-10-16
Category : Medical
ISBN : 9781799850724

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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 Healthcare

Yen-Wei Chen,Lakhmi C. Jain
Deep Learning in Healthcare Book PDF

Author : Yen-Wei Chen,Lakhmi C. Jain
Publisher : Springer Nature
Page : 225 pages
File Size : 44,7 Mb
Release : 2019-11-18
Category : Technology & Engineering
ISBN : 9783030326067

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Deep Learning in Healthcare by Yen-Wei Chen,Lakhmi C. Jain Pdf

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 Learners and Deep Learner Descriptors for Medical Applications

Loris Nanni,Sheryl Brahnam,Rick Brattin,Stefano Ghidoni,Lakhmi C. Jain
Deep Learners and Deep Learner Descriptors for Medical Applications Book PDF

Author : Loris Nanni,Sheryl Brahnam,Rick Brattin,Stefano Ghidoni,Lakhmi C. Jain
Publisher : Springer Nature
Page : 286 pages
File Size : 55,5 Mb
Release : 2020-05-15
Category : Technology & Engineering
ISBN : 9783030427504

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Deep Learners and Deep Learner Descriptors for Medical Applications by Loris Nanni,Sheryl Brahnam,Rick Brattin,Stefano Ghidoni,Lakhmi C. Jain Pdf

This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.

Deep Learning for Medical Applications with Unique Data

Deepak Gupta,Utku Kose,Ashish Khanna,Valentina Emilia Balas
Deep Learning for Medical Applications with Unique Data Book PDF

Author : Deepak Gupta,Utku Kose,Ashish Khanna,Valentina Emilia Balas
Publisher : Academic Press
Page : 258 pages
File Size : 50,6 Mb
Release : 2022-02-15
Category : Science
ISBN : 9780128241462

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Deep Learning for Medical Applications with Unique Data by Deepak Gupta,Utku Kose,Ashish Khanna,Valentina Emilia Balas Pdf

Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications

Deep Learning for Medical Decision Support Systems

Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut
Deep Learning for Medical Decision Support Systems Book PDF

Author : Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut
Publisher : Springer Nature
Page : 185 pages
File Size : 54,9 Mb
Release : 2020-06-17
Category : Technology & Engineering
ISBN : 9789811563256

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Deep Learning for Medical Decision Support Systems by Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut Pdf

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.

Computational Intelligence And Image Processing In Medical Applications

Chi Hau Chen
Computational Intelligence And Image Processing In Medical Applications Book PDF

Author : Chi Hau Chen
Publisher : World Scientific
Page : 336 pages
File Size : 46,7 Mb
Release : 2022-05-30
Category : Computers
ISBN : 9789811257469

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Computational Intelligence And Image Processing In Medical Applications by Chi Hau Chen Pdf

In recent years, there have been significant progress in computational intelligence and image processing with machine learning and deep learning as important components of modern artificial intelligence. All these progresses face challenges in dealing with Covid-19 pandemic for detection and treatment.This comprehensive compendium provides not only updated advances of computational intelligence and image processing in the detection and treatment of Covid-19, but also other medical applications such as in cancer detection and cardiovascular diseases, etc. More traditional approaches such as 2D segmentation and 3D reconstruction are included.The useful reference text is an updated version of the edited title, Computer Vision in Medical Imaging (World Scientific, 2014) and its companion volume, Frontiers of Medical Imaging (World Scientific, 2015). The book is written for engineers, scientists and the medical community to meet the increased challenges in medical applications.