Resources
Titles in the Series
Trustworthy AI in Medical Imaging
Marco Lorenzi and Maria A. Zuluaga
Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications.
Federated Learning for Medical Imaging
Xiaxiao Li, Ziyue Xu and Huazhu Fu
Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging.
Biomedical Image Synthesis and Simulation
Burgos, N. and Svoboda, D.
Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. It introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models, giving examples of successful applications of these methods.
Radiomics and Its Clinical Application
Jie Tian, Di Dong, Zhenyu Liu, Jingwei Wei
The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field.
Deep Network Design for Medical Image Computing
Haofu Liao, S. Kevin Zhou, Jiebo Luo
Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc.
Meta Learning with Medical Imaging and Health Informatics Applications
Hien Van Nguyen, Ronald Summers, Rama Chellappa
This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize.
Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications
Bernhard Preim, Renata Raidou, Noeska Smit, Kai Lawonn
Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research for task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation.
Medical Image Analysis
Alejandro Frangi, Jerry Prince, Milan Sonka
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.
Computational Retinal Image Analysis: Tools, Applications and Perspectives
Emanuele Trucco, Tom MacGillivray, Yanwu Xu
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data.
Riemannian Geometric Statistics in Medical Image Analysis
Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with application to medical image It provides an introduction to a core methodology followed by a presentation of state-of-the- art methods.
Handbook of Medical Image Computing and Computer Assisted Intervention
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential.
Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches
Learn and apply methods and algorithms for automatically recognizing, segmenting and parsing multiple objects.
Machine Learning and Medical Imaging
Learn how to apply machine learning methods to medical imaging.
Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting
Applies deep learning methods to medical imaging, providing a clear understanding of the principles and methods of neural network and deep learning.
Deep Learning for Medical Image 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.
Biomedical Texture Analysis: Fundamentals, Tools and Challenges
Biomedical Texture Analysis: Fundamentals,Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications.
Imaging Genetics
Imaging Genetics presents the latest research in imaging genetics methodology for discovering new associations between imaging and genetic variables, providing an overview of the state-of-the-art in the field.
Connectomics: Applications to Neuroimaging
The book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer’s, epilepsy, stroke, autism, Parkinson’s, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain fingerprint applications, speech-language assessments, and cognitive assessment.