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| data science for medical imaging: Machine Learning and Medical Imaging Guorong Wu, Dinggang Shen, Mert Sabuncu, 2016-08-11 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 |
| data science for medical imaging: AI Innovation in Medical Imaging Diagnostics Anbarasan, Kalaivani, 2021-01-01 Recent advancements in the technology of medical imaging, such as CT and MRI scanners, are making it possible to create more detailed 3D and 4D images. These powerful images require vast amounts of digital data to help with the diagnosis of the patient. Artificial intelligence (AI) must play a vital role in supporting with the analysis of this medical imaging data, but it will only be viable as long as healthcare professionals and AI interact to embrace deep thinking platforms such as automation in the identification of diseases in patients. AI Innovation in Medical Imaging Diagnostics is an essential reference source that examines AI applications in medical imaging that can transform hospitals to become more efficient in the management of patient treatment plans through the production of faster imaging and the reduction of radiation dosages through the PET and SPECT imaging modalities. The book also explores how data clusters from these images can be translated into small data packages that can be accessed by healthcare departments to give a real-time insight into patient care and required interventions. Featuring research on topics such as assistive healthcare, cancer detection, and machine learning, this book is ideally designed for healthcare administrators, radiologists, data analysts, computer science professionals, medical imaging specialists, diagnosticians, medical professionals, researchers, and students. |
| data science for medical imaging: Artificial Intelligence in Medical Imaging Erik R. Ranschaert, Sergey Morozov, Paul R. Algra, 2019-01-29 This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals. |
| data science for medical imaging: Medical Imaging K.C. Santosh, Sameer Antani, DS Guru, Nilanjan Dey, 2019-08-20 Winner of the Outstanding Academic Title recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. 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. |
| data science for medical imaging: Introduction to the Science of Medical Imaging R. Nick Bryan, 2010 This landmark text from world-leading radiologist describes and illustrates how imaging techniques are created, analyzed and applied to biomedical problems. |
| data science for medical imaging: Deep Learning Applications in Medical Imaging Saxena, Sanjay, Paul, Sudip, 2020-10-16 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. |
| data science for medical imaging: Machine Learning in Medical Imaging Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan, 2021-09-25 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. |
| data science for medical imaging: Deep Learning Models for Medical Imaging KC Santosh, Nibaran Das, Swarnendu Ghosh, 2021-09-07 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) |
| data science for medical imaging: Medical Image Analysis Alejandro Frangi, Jerry Prince, Milan Sonka, 2023-09-20 Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing |
| data science for medical imaging: Handbook of Medical Imaging , 2000-10-09 In recent years, the remarkable advances in medical imaging instruments have increased their use considerably for diagnostics as well as planning and follow-up of treatment. Emerging from the fields of radiology, medical physics and engineering, medical imaging no longer simply deals with the technology and interpretation of radiographic images. The limitless possibilities presented by computer science and technology, coupled with engineering advances in signal processing, optics and nuclear medicine have created the vastly expanded field of medical imaging. The Handbook of Medical Imaging is the first comprehensive compilation of the concepts and techniques used to analyze and manipulate medical images after they have been generated or digitized. The Handbook is organized in six sections that relate to the main functions needed for processing: enhancement, segmentation, quantification, registration, visualization as well as compression storage and telemedicine. * Internationally renowned authors(Johns Hopkins, Harvard, UCLA, Yale, Columbia, UCSF) * Includes imaging and visualization * Contains over 60 pages of stunning, four-color images |
| data science for medical imaging: Artificial Intelligence in Medical Imaging Lia Morra, Silvia Delsanto, Loredana Correale, 2019-11-25 Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective |
| data science for medical imaging: Medical Imaging Informatics Mamoon Rashid, Vishal Goyal, Ali Kashif Bashir, Saqib Hakak, 2024-01-09 Medical Imaging Informatics is an edited book that discusses how medical images can be processed using machine learning techniques and big data analysis methods. These tools help physicians to gain a full overview of a patient's data, which in turn assists with diagnosis, prognosis or intervention. |
| data science for medical imaging: Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance Rana, Dipti P., Mehta, Rupa G., 2021-06-04 Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches. |
| data science for medical imaging: Deep Learning in Medical Image Analysis Gobert Lee, Hiroshi Fujita, 2020-02-06 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. |
| data science for medical imaging: Medical Imaging Systems Andreas Maier, Stefan Steidl, Vincent Christlein, Joachim Hornegger, 2018-08-02 This open access book gives a complete and comprehensive introduction to the fields of medical imaging systems, as designed for a broad range of applications. The authors of the book first explain the foundations of system theory and image processing, before highlighting several modalities in a dedicated chapter. The initial focus is on modalities that are closely related to traditional camera systems such as endoscopy and microscopy. This is followed by more complex image formation processes: magnetic resonance imaging, X-ray projection imaging, computed tomography, X-ray phase-contrast imaging, nuclear imaging, ultrasound, and optical coherence tomography. |
| data science for medical imaging: Knowledge Modelling and Big Data Analytics in Healthcare Mayuri Mehta, Kalpdrum Passi, Indranath Chatterjee, Rajan Patel, 2021-12-08 Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications focuses on automated analytical techniques for healthcare applications used to extract knowledge from a vast amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals. The editors connect four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. They present state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery. This book is intended for data scientists, academicians, and industry professionals in the healthcare sector. |
| data science for medical imaging: Introduction to Medical Image Analysis Rasmus R. Paulsen, Thomas B. Moeslund, 2020-05-26 This easy-to-follow textbook presents an engaging introduction to the fascinating world of medical image analysis. Avoiding an overly mathematical treatment, the text focuses on intuitive explanations, illustrating the key algorithms and concepts in a way which will make sense to students from a broad range of different backgrounds. Topics and features: explains what light is, and how it can be captured by a camera and converted into an image, as well as how images can be compressed and stored; describes basic image manipulation methods for understanding and improving image quality, and a useful segmentation algorithm; reviews the basic image processing methods for segmenting or enhancing certain features in an image, with a focus on morphology methods for binary images; examines how to detect, describe, and recognize objects in an image, and how the nature of color can be used for segmenting objects; introduces a statistical method to determine what class of object the pixels in an image represent; describes how to change the geometry within an image, how to align two images so that they are as similar as possible, and how to detect lines and paths in images; provides further exercises and other supplementary material at an associated website. This concise and accessible textbook will be invaluable to undergraduate students of computer science, engineering, medicine, and any multi-disciplinary courses that combine topics on health with data science. Medical practitioners working with medical imaging devices will also appreciate this easy-to-understand explanation of the technology. |
| data science for medical imaging: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| data science for medical imaging: Deep Learning for Medical Image Analysis S. Kevin Zhou, Hayit Greenspan, Dinggang Shen, 2023-11-23 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 |
| data science for medical imaging: Radiomics and Radiogenomics Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, 2019-07-09 Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation |
| data science for medical imaging: Basic Knowledge of Medical Imaging Informatics Peter M. A. van Ooijen, 2021-05-27 This book provides a unique introduction to the vast field of Medical Imaging Informatics for students and physicians by depicting the basics of the different areas in Radiology Informatics. It features short chapters on the different main areas in Medical Imaging Informatics, such as Picture Archiving and Communication Systems (PACS), radiology reporting, data sharing, and de-identification and anonymization, as well as standards like Digital Imaging and Communications in Medicine (DICOM), Integrating the Health Enterprise (IHE) and Health Level 7 (HL7,. Written by experts in the respective fields and endorsed by the European Society of Medical Imaging Informatics (EuSoMII) the scope of the book is based on the Medical Imaging Informatics sub-sections of the European Society of Radiology (ESR) European Training Curriculum Undergraduate Level and Level I. This volume will be an invaluable resource for residents and radiologists and is also specifically suited for undergraduate training. |
| data science for medical imaging: Healthcare Data Analytics Chandan K. Reddy, Charu C. Aggarwal, 2015-06-23 At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available |
| data science for medical imaging: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures Hayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, Miguel Ángel González Ballester, 2019-10-10 This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 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. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. |
| data science for medical imaging: Big Data in Multimodal Medical Imaging Ayman El-Baz, Jasjit S. Suri, 2019-11-05 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. |
| data science for medical imaging: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| data science for medical imaging: Medical Content-Based Retrieval for Clinical Decision Support Hayit Greenspan, Henning Mèuller, Tanveer Syeda-Mahmood, 2013 This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012. The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data), machine learning of disease correlations in visual or multimodal data, algorithms for indexing and retrieval of data from visual or multimodal medical databases, disease model-building and clinical decision support systems based on visual or multimodal analysis, algorithms for medical image retrieval or classification, systems of retrieval or classification using the ImageCLEF collection. |
| data science for medical imaging: Introduction to Deep Learning for Healthcare Cao Xiao, Jimeng Sun, 2021-11-11 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. |
| data science for medical imaging: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot, 2020-10-05 This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 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. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. |
| data science for medical imaging: Advanced Machine Vision Paradigms for Medical Image Analysis Tapan K. Gandhi, Siddhartha Bhattacharyya, Sourav De, Debanjan Konar, Sandip Dey, 2020-08-11 Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. - Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence - Highlights the advancement of conventional approaches in the field of medical image processing - Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis |
| data science for medical imaging: Advances in Computerized Analysis in Clinical and Medical Imaging J Dinesh Peter, Steven Lawrence Fernandes, Carlos Eduardo Thomaz, 2019-10-28 Advances in Computerized Analysis in Clinical and Medical Imaging book is devoted for spreading of knowledge through the publication of scholarly research, primarily in the fields of clinical & medical imaging. The types of chapters consented include those that cover the development and implementation of algorithms and strategies based on the use of geometrical, statistical, physical, functional to solve the following types of problems, using medical image datasets: visualization, feature extraction, segmentation, image-guided surgery, representation of pictorial data, statistical shape analysis, computational physiology and telemedicine with medical images. This book highlights annotations for all the medical and clinical imaging researchers’ a fundamental advances of clinical and medical image analysis techniques. This book will be a good source for all the medical imaging and clinical research professionals, outstanding scientists, and educators from all around the world for network of knowledge sharing. This book will comprise high quality disseminations of new ideas, technology focus, research results and discussions on the evolution of Clinical and Medical image analysis techniques for the benefit of both scientific and industrial developments. Features: Research aspects in clinical and medical image processing Human Computer Interaction and interface in imaging diagnostics Intelligent Imaging Systems for effective analysis using machine learning algorithms Clinical and Scientific Evaluation of Imaging Studies Computer-aided disease detection and diagnosis Clinical evaluations of new technologies Mobility and assistive devices for challenged and elderly people This book serves as a reference book for researchers and doctoral students in the clinical and medical imaging domain including radiologists. Industries that manufacture imaging modality systems and develop optical systems would be especially interested in the challenges and solutions provided in the book. Professionals and practitioners in the medical and clinical imaging may be benefited directly from authors’ experiences. |
| data science for medical imaging: Medical Imaging Informatics Alex A.T. Bui, Ricky K. Taira, 2009-12-01 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. |
| data science for medical imaging: Mathematics and Computer Science in Medical Imaging Max A. Viergever, Andrew Todd-Pokropek, 2012-12-06 Medical imaging is an important and rapidly expanding area in medical science. Many of the methods employed are essentially digital, for example computerized tomography, and the subject has become increasingly influenced by develop ments in both mathematics and computer science. The mathematical problems have been the concern of a relatively small group of scientists, consisting mainly of applied mathematicians and theoretical physicists. Their efforts have led to workable algorithms for most imaging modalities. However, neither the fundamentals, nor the limitations and disadvantages of these algorithms are known to a sufficient degree to the physicists, engineers and physicians trying to implement these methods. It seems both timely and important to try to bridge this gap. This book summarizes the proceedings of a NATO Advanced Study Institute, on these topics, that was held in the mountains of Tuscany for two weeks in the late summer of 1986. At another (quite different) earlier meeting on medical imaging, the authors noted that each of the speakers had given, there, a long introduction in their general area, stated that they did not have time to discuss the details of the new work, but proceeded to show lots of clinical results, while excluding any mathematics associated with the area. |
| data science for medical imaging: Fundamentals of Medical Imaging Paul Suetens, 2009-08-06 Fundamentals of Medical Imaging, second edition, is an invaluable technical introduction to each imaging modality, explaining the mathematical and physical principles and giving a clear understanding of how images are obtained and interpreted. Individual chapters cover each imaging modality – radiography, CT, MRI, nuclear medicine and ultrasound – reviewing the physics of the signal and its interaction with tissue, the image formation or reconstruction process, a discussion of image quality and equipment, clinical applications and biological effects and safety issues. Subsequent chapters review image analysis and visualization for diagnosis, treatment and surgery. New to this edition: • Appendix of questions and answers • New chapter on 3D image visualization • Advanced mathematical formulae in separate text boxes • Ancillary website containing 3D animations: www.cambridge.org/suetens • Full colour illustrations throughout Engineers, clinicians, mathematicians and physicists will find this an invaluable aid in understanding the physical principles of imaging and their clinical applications. |
| data science for medical imaging: Informatics in Medical Imaging George C. Kagadis, Steve G. Langer, 2020-06-30 Informatics in Medical Imaging provides a comprehensive survey of the field of medical imaging informatics. In addition to radiology, it also addresses other specialties such as pathology, cardiology, dermatology, and surgery, which have adopted the use of digital images. The book discusses basic imaging informatics protocols, picture archiving and communication systems, and the electronic medical record. It details key instrumentation and data mining technologies used in medical imaging informatics as well as practical operational issues, such as procurement, maintenance, teleradiology, and ethics. Highlights Introduces the basic ideas of imaging informatics, the terms used, and how data are represented and transmitted Emphasizes the fundamental communication paradigms: HL7, DICOM, and IHE Describes information systems that are typically used within imaging departments: orders and result systems, acquisition systems, reporting systems, archives, and information-display systems Outlines the principal components of modern computing, networks, and storage systems Covers the technology and principles of display and acquisition detectors, and rounds out with a discussion of other key computer technologies Discusses procurement and maintenance issues; ethics and its relationship to government initiatives like HIPAA; and constructs beyond radiology The technologies of medical imaging and radiation therapy are so complex and computer-driven that it is difficult for physicians and technologists responsible for their clinical use to know exactly what is happening at the point of care. Medical physicists are best equipped to understand the technologies and their applications, and these individuals are assuming greater responsibilities in the clinical arena to ensure that intended care is delivered in a safe and effective manner. Built on a foundation of classic and cutting-edge research, Informatics in Medical Imaging supports and updates medical physicists functioning at the intersection of radiology and radiation. |
| data science for medical imaging: Approaches and Applications of Deep Learning in Virtual Medical Care Zaman, Noor, Gaur, Loveleen, Humayun, Mamoona, 2022-02-25 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. |
| data science for medical imaging: Healthcare Data Analytics and Management Nilanjan Dey, Amira S. Ashour, Simon James Fong, Chintan Bhatt, 2018-11-15 Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data. - Covers data analysis, management and security concepts and tools in the healthcare domain - Highlights electronic medical health records and patient information records - Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining - Includes multidisciplinary contributions in relation to healthcare applications and challenges |
| data science for medical imaging: Challenges and Applications for Implementing Machine Learning in Computer Vision Kashyap, Ramgopal, Kumar, A.V. Senthil, 2019-10-04 Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques. |
| data science for medical imaging: How Data Science Is Transforming Health Care Tim O'Reilly, Mike Loukides, Julie Steele, Colin Hill, 2012-08-24 In the early days of the 20th century, department store magnate JohnWanamaker famously said, I know that half of my advertising doesn'twork. The problem is that I don't know which half. That remainedbasically true until Google transformed advertising with AdSense basedon new uses of data and analysis. The same might be said about healthcare and it's poised to go through a similar transformation as newtools, techniques, and data sources come on line. Soon we'll makepolicy and resource decisions based on much better understanding ofwhat leads to the best outcomes, and we'll make medical decisionsbased on a patient's specific biology. The result will be betterhealth at less cost. This paper explores how data analysis will help us structure thebusiness of health care more effectively around outcomes, and how itwill transform the practice of medicine by personalizing for eachspecific patient. |
| data science for medical imaging: Big Data in Radiation Oncology Jun Deng, Lei Xing, 2019-03-07 Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship. |
| data science for medical imaging: Molybdenum-99 for Medical Imaging National Academies of Sciences, Engineering, and Medicine, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Committee on State of Molybdenum-99 Production and Utilization and Progress Toward Eliminating Use of Highly Enriched Uranium, 2016-11-28 The decay product of the medical isotope molybdenum-99 (Mo-99), technetium-99m (Tc-99m), and associated medical isotopes iodine-131 (I-131) and xenon-133 (Xe-133) are used worldwide for medical diagnostic imaging or therapy. The United States consumes about half of the world's supply of Mo-99, but there has been no domestic (i.e., U.S.-based) production of this isotope since the late 1980s. The United States imports Mo-99 for domestic use from Australia, Canada, Europe, and South Africa. Mo-99 and Tc-99m cannot be stockpiled for use because of their short half-lives. Consequently, they must be routinely produced and delivered to medical imaging centers. Almost all Mo-99 for medical use is produced by irradiating highly enriched uranium (HEU) targets in research reactors, several of which are over 50 years old and are approaching the end of their operating lives. Unanticipated and extended shutdowns of some of these old reactors have resulted in severe Mo-99 supply shortages in the United States and other countries. Some of these shortages have disrupted the delivery of medical care. Molybdenum-99 for Medical Imaging examines the production and utilization of Mo-99 and associated medical isotopes, and provides recommendations for medical use. |
Preparing Medical Imaging Data for Machine Learning
In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new …
Augmenting Medical Imaging: A Comprehensive Catalogue of …
We conducted an in-depth study of all data augmentation techniques used in medical imaging, identifying 11 different purposes and collecting 65 distinct techniques.
Development of Medical Imaging Data Standardization for …
Imaging researchers focus on obtaining knowledge from medical images, while observational researchers rely on structured information from electronic health records (EHRs). Accessing …
Deep Learning Approaches for Medical Imaging Under …
We examine multiple medical imaging tasks (classifica-tion, segmentation, and object detection) across a wide range of imaging modalities (MRI, CT, X-ray, US, and more).
Fusion of medical imaging and electronic health records using …
In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published...
Medical Imaging data standards, automation and analysis
Building a data driven culture and trust around data analysis is essential for long term success. Medical Imaging forms a significant part of most clinical trials. Help determine the safety and …
Differential Data Augmentation Techniques for Medical …
In this work, we study different strategies for binary image classification of mass and non-mass mammogram images. We show that some augmentation meth-ods capture medical image …
Data preparation for artificial intelligence in medical imaging: …
Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms.
A Review of Three-Dimensional Medical Image Visualization
Our review provides a quick reference to such techniques given fi the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily …
Deep Learning in Multimodal Medical Image Analysis - Springer
This paper gives a review of deep learning in multimodal medical imaging analysis, aiming to provide a starting point for people interested in this field, and highlight gaps and challenges of …
Deep Learning Approaches for Data Augmentation in Medical …
Jul 26, 2023 · In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion …
Shielding sensitive medical imaging data - Nature
The integration of AI in medical imaging holds immense potential for advancing healthcare, offering capabilities such as accurate detec- tion of diseases and better prediction of patient...
Overcoming data scarcity in biomedical imaging with a
In this Article, to combine multiple datasets with different label types for large-scale pretraining, we introduce a multi-task training strategy and a corresponding model architecture specifically...
Lecture 3: Medical Imaging - GitHub Pages
What kind of image data is there in medicine? Radiologists: collection and interpretation of medical imaging data (including using imaging to guide minimally-invasive procedures). Often …
Health & Life Sciences Data Driven Using AI in Medical …
Health & Life Sciences Data Driven Using AI in Medical Imaging to Improve Accuracy and Efficiency. Advances in deep learning (DL) and other artificial intelligence (AI) methodologies …
Review of Artificial Intelligence Techniques in Imaging Data ...
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) …
Medical Image Synthesis for Data Augmentation and
In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. …
Knowledge Graph Applications in Medical Imaging Analysis: A …
Oct 31, 2021 · We looked at four applications in medical imaging analysis, including disease classi fication, disease localization and segmentation, report generation, and image retrieval. We also …
Medical Imaging: Data Sources and Formats - HPI
What is a Medical Diagnose? Clinical diagnosis Laboratory diagnosis ... Example: What types of diagnostic tests exist to detect COVID-19? What is Radiology? , , - X-ray attenuation …
Medical image data augmentation: techniques, comparisons …
comprehensive review that covers a greater range of medical imaging modalities and applications than articles in the literature, and (ii) any work on quantitative evaluations of the augmentation …
Preparing Medical Imaging Data for Machine Learning
In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new …
Augmenting Medical Imaging: A Comprehensive Catalogue …
We conducted an in-depth study of all data augmentation techniques used in medical imaging, identifying 11 different purposes and collecting 65 distinct techniques.
Development of Medical Imaging Data Standardization for …
Imaging researchers focus on obtaining knowledge from medical images, while observational researchers rely on structured information from electronic health records (EHRs). Accessing …
Deep Learning Approaches for Medical Imaging Under …
We examine multiple medical imaging tasks (classifica-tion, segmentation, and object detection) across a wide range of imaging modalities (MRI, CT, X-ray, US, and more).
Fusion of medical imaging and electronic health records …
In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published...
Medical Imaging data standards, automation and analysis
Building a data driven culture and trust around data analysis is essential for long term success. Medical Imaging forms a significant part of most clinical trials. Help determine the safety and …
Differential Data Augmentation Techniques for Medical …
In this work, we study different strategies for binary image classification of mass and non-mass mammogram images. We show that some augmentation meth-ods capture medical image …
Data preparation for artificial intelligence in medical …
Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms.
A Review of Three-Dimensional Medical Image Visualization
Our review provides a quick reference to such techniques given fi the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily …
Deep Learning in Multimodal Medical Image Analysis - Springer
This paper gives a review of deep learning in multimodal medical imaging analysis, aiming to provide a starting point for people interested in this field, and highlight gaps and challenges of …
Deep Learning Approaches for Data Augmentation in …
Jul 26, 2023 · In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion …
Shielding sensitive medical imaging data - Nature
The integration of AI in medical imaging holds immense potential for advancing healthcare, offering capabilities such as accurate detec- tion of diseases and better prediction of patient...
Overcoming data scarcity in biomedical imaging with a
In this Article, to combine multiple datasets with different label types for large-scale pretraining, we introduce a multi-task training strategy and a corresponding model architecture specifically...
Lecture 3: Medical Imaging - GitHub Pages
What kind of image data is there in medicine? Radiologists: collection and interpretation of medical imaging data (including using imaging to guide minimally-invasive procedures). Often …
Health & Life Sciences Data Driven Using AI in Medical …
Health & Life Sciences Data Driven Using AI in Medical Imaging to Improve Accuracy and Efficiency. Advances in deep learning (DL) and other artificial intelligence (AI) methodologies …
Review of Artificial Intelligence Techniques in Imaging Data ...
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) …
Medical Image Synthesis for Data Augmentation and
In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain …
Knowledge Graph Applications in Medical Imaging Analysis: …
Oct 31, 2021 · We looked at four applications in medical imaging analysis, including disease classi fication, disease localization and segmentation, report generation, and image retrieval. …
Medical Imaging: Data Sources and Formats - HPI
What is a Medical Diagnose? Clinical diagnosis Laboratory diagnosis ... Example: What types of diagnostic tests exist to detect COVID-19? What is Radiology? , , - X-ray attenuation …
Medical image data augmentation: techniques, comparisons …
comprehensive review that covers a greater range of medical imaging modalities and applications than articles in the literature, and (ii) any work on quantitative evaluations of the augmentation …
