Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging
Author: K.C. Santosh,Nibaran Das,Swarnendu Ghosh
Publsiher: Academic Press
Total Pages: 170
Release: 2021-09-17
ISBN: 0128236507
Category: Computers
Language: EN, FR, DE, ES & NL

Deep Learning Models for Medical Imaging Book Excerpt:

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation (source codes: available upon request)

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
Publsiher: Springer
Total Pages: 326
Release: 2017-07-12
ISBN: 331942999X
Category: Computers
Language: EN, FR, DE, ES & NL

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book Excerpt:

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

Introduction to Deep Learning for Healthcare
Author: Cao Xiao,Jimeng Sun
Publsiher: Springer Nature
Total Pages: 232
Release: 2021-11-11
ISBN: 3030821846
Category: Medical
Language: EN, FR, DE, ES & NL

Introduction to Deep Learning for Healthcare Book Excerpt:

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.

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
Author: Archana Mire,Vinayak Elangovan,Shailaja Patil
Publsiher: CRC Press
Total Pages: 168
Release: 2022-04-28
ISBN: 1000575950
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Advances in Deep Learning for Medical Image Analysis Book Excerpt:

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Generalizable and Explainable Deep Learning in Medical Imaging with Small Data

Generalizable and Explainable Deep Learning in Medical Imaging with Small Data
Author: Hyunkwang Lee
Publsiher: Unknown
Total Pages: 189
Release: 2021
ISBN: 1928374650XXX
Category: Artificial Intelligence
Language: EN, FR, DE, ES & NL

Generalizable and Explainable Deep Learning in Medical Imaging with Small Data Book Excerpt:

Deep learning algorithms, such as those used for image recognition, holds promise for automated medical diagnosis and in guiding clinical decision-making. At the same time, there remain several important challenges to the development and clinical translation of medical deep learning systems. First, it is costly to develop large and well-annotated datasets. Second, it is necessary for medical image interpretation to identify subtle key features for lesions despite the wide range in physiologic appearance across the population. Third, it is challenging to transfer the performance of deep learning algorithms from one setting to another because of domain shift problems. Fourth, the outputs of deep learning systems need to be explainable in order to make the systems understandable to clinicians. This dissertation investigates how to address these challenges, building generalizable and explainable deep learning models from small datasets. The thesis studies the impact on model performance of transferring prior knowledge learned from a non-medical source — ImageNet — to medical applications, especially when the dataset size is not sufficient. Instead of direct transfer learning from ImageNet, GrayNet is proposed as a bridge dataset to create a pre-trained model enriched with medical image representations on top of the generic image features learned from ImageNet. Benefits of GrayNet are analyzed with regard to overall performance and generalization across different imaging scanners, in comparison with training from scratch with small data and transfer learning from ImageNet. Domain-specific techniques including window setting optimization and slice interpolation, inspired by how radiologists interpret images for a diagnosis, are also introduced and shown to further enhance model performance. A new visualization module is introduced, able to generate an atlas of images during training, and display this as the basis of model predictions made during testing, in order to justify model predictions and make them more understandable for clinicians. This thesis demonstrates the potential of deep learning for medical image interpretation through three different applications, including AI-assisted bone age assessment to improve human’s accuracy and variability, finding previously unrecognized patterns to perform bone sex classification in hand radiographs, and processing raw computed tomography data without image reconstruction. The contributions of this thesis are expected to facilitate the development of generalizable and explainable deep learning algorithms for a variety of medical applications and consequently accelerate the adoption of AI systems into clinical practice.

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
Publsiher: Springer Nature
Total Pages: 461
Release: 2019-09-19
ISBN: 3030139697
Category: Computers
Language: EN, FR, DE, ES & NL

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics Book Excerpt:

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.

Simulation and Synthesis in Medical Imaging

Simulation and Synthesis in Medical Imaging
Author: Ali Gooya,Orcun Goksel,Ipek Oguz,Ninon Burgos
Publsiher: Springer
Total Pages: 140
Release: 2018-09-11
ISBN: 3030005364
Category: Computers
Language: EN, FR, DE, ES & NL

Simulation and Synthesis in Medical Imaging Book Excerpt:

This book constitutes the refereed proceedings of the Third International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 14 full papers presented were carefully reviewed and selected from numerous submissions. This workshop continues to provide a state-of-the-art and integrative perspective on simulation and synthesis in medical imaging for the purpose of invigorating research and stimulating new ideas on how to build theoretical links, practical synergies, and best practices between these two research directions.

Approaches and Applications of Deep Learning in Virtual Medical Care

Approaches and Applications of Deep Learning in Virtual Medical Care
Author: Zaman, Noor,Gaur, Loveleen,Humayun, Mamoona
Publsiher: IGI Global
Total Pages: 293
Release: 2022-02-25
ISBN: 1799889300
Category: Computers
Language: EN, FR, DE, ES & NL

Approaches and Applications of Deep Learning in Virtual Medical Care Book Excerpt:

The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian
Publsiher: Springer Nature
Total Pages: 695
Release: 2019-10-09
ISBN: 3030326926
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Excerpt:

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
Author: Gobert Lee,Hiroshi Fujita
Publsiher: Springer Nature
Total Pages: 181
Release: 2020-02-06
ISBN: 3030331288
Category: Medical
Language: EN, FR, DE, ES & NL

Deep Learning in Medical Image Analysis Book Excerpt:

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.

Medical Imaging

Medical Imaging
Author: K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publsiher: CRC Press
Total Pages: 238
Release: 2019-08-20
ISBN: 0429639325
Category: Computers
Language: EN, FR, DE, ES & NL

Medical Imaging Book Excerpt:

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.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Qian Wang,Yinghuan Shi,Heung-Il Suk,Kenji Suzuki
Publsiher: Springer
Total Pages: 391
Release: 2017-09-06
ISBN: 3319673890
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Excerpt:

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

Big Data in Multimodal Medical Imaging

Big Data in Multimodal Medical Imaging
Author: Ayman El-Baz,Jasjit S. Suri
Publsiher: CRC Press
Total Pages: 330
Release: 2019-11-05
ISBN: 1351380737
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data in Multimodal Medical Imaging Book Excerpt:

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Deep Learning Applications in Medical Imaging

Deep Learning Applications in Medical Imaging
Author: Saxena, Sanjay,Paul, Sudip
Publsiher: IGI Global
Total Pages: 274
Release: 2020-10-16
ISBN: 1799850722
Category: Medical
Language: EN, FR, DE, ES & NL

Deep Learning Applications in Medical Imaging Book Excerpt:

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.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
Author: Guorong Wu,Dinggang Shen,Mert Sabuncu
Publsiher: Academic Press
Total Pages: 512
Release: 2016-08-11
ISBN: 0128041145
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Machine Learning and Medical Imaging Book Excerpt:

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Chunfeng Lian,Xiaohuan Cao,Islem Rekik,Xuanang Xu,Pingkun Yan
Publsiher: Springer Nature
Total Pages: 704
Release: 2021-09-25
ISBN: 303087589X
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Excerpt:

This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Perinatal Imaging Placental and Preterm Image Analysis

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging  and Perinatal Imaging  Placental and Preterm Image Analysis
Author: Carole H. Sudre,Roxane Licandro,Christian Baumgartner,Andrew Melbourne,Adrian Dalca,Jana Hutter,Ryutaro Tanno,Esra Abaci Turk,Koen Van Leemput,Jordina Torrents Barrena,William M. Wells,Christopher Macgowan
Publsiher: Springer Nature
Total Pages: 306
Release: 2021-09-30
ISBN: 3030877353
Category: Computers
Language: EN, FR, DE, ES & NL

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Perinatal Imaging Placental and Preterm Image Analysis Book Excerpt:

This book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Machine and Deep Learning in Oncology Medical Physics and Radiology

Machine and Deep Learning in Oncology  Medical Physics and Radiology
Author: Issam El Naqa,Martin J. Murphy (Ph. D.)
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN: 3030830470
Category: Electronic books
Language: EN, FR, DE, ES & NL

Machine and Deep Learning in Oncology Medical Physics and Radiology Book Excerpt:

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities. .

Deep Learning for Biomedical Applications

Deep Learning for Biomedical Applications
Author: Utku Kose,Omer Deperlioglu,D. Jude Hemanth
Publsiher: CRC Press
Total Pages: 364
Release: 2021-07-20
ISBN: 1000406423
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Deep Learning for Biomedical Applications Book Excerpt:

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Yinghuan Shi,Heung-Il Suk,Mingxia Liu
Publsiher: Springer
Total Pages: 409
Release: 2018-09-14
ISBN: 303000919X
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Excerpt:

This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.