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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.
Fast-emerging optical imaging and data fusion techniques are creating a huge buzz as potentially powerful tools for cancer detection and other important clinical applications. Heres the first comprehensive resource on multimodal optical imaging. This unique book gives biomedical researchers the full details on in-vivo microscopy, optical coherence tomography, endoscopy, and diffuse optical techniques. It explains how these tools can be integrated with other modalities to capture and apply biological information in significant new ways. Written by pioneers in the field, this milestone work discusses each of the optical imaging techniques in depth and describes the integration of the techniques with other modalities like MRI, X-Ray imaging, and PET imaging. The volume also covers photodynamic therapy and molecular imaging, and reviews clinical studies in optical imaging and other issues. DVD Included! Contains full-color images and videos that support key topics covered in the book.
This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
Most books discuss general and broad topics regarding molecular imagings. However, Ultrasmall Lanthanide Oxide Nanoparticles for Biomedical Imaging and Therapy, will mainly focus on lanthanide oxide nanoparticles for molecular imaging and therapeutics. Multi-modal imaging capabilities will discussed, along with up-converting FI by using lanthanide oxide nanoparticles. The synthesis will cover polyol synthesis of lanthanide oxide nanoparticles, Surface coatings with biocompatible and hydrophilic ligands will be discussed and TEM images and dynamic light scattering (DLS) patterns will be provided. Various techniques which are generally used in analyzing the synthesized surface coated nanoparticles will be explored and this section will also cover FT, IR analysis, XRD analysis, SQUID analysis, cytotoxicity measurements and proton relaxivity measurements. In vivo MR images, CT images, fluorescence images will be provided and Therapeutic application of gadolinium oxide nanoparticles will be discussed. Finally, future perpectives will be discussed. That is, present status and future works needed for clinical applications of lanthanide oxide nanoparticles to molecular imagings will be discussed. - Synthesis will be discussed in detail - General characterizations of nanoparticles before in vivo applications will be discussed - The book will cover all possible applications of lanthanide oxide nanoparticles to molecular imagings such as MRI, CT, FI as well as therapeutics
This book provides an introduction to design of biomedical optical imaging technologies and their applications. The main topics include: fluorescence imaging, confocal imaging, micro-endoscope, polarization imaging, hyperspectral imaging, OCT imaging, multimodal imaging and spectroscopic systems. Each chapter is written by the world leaders of the respective fields, and will cover: principles and limitations of optical imaging technology, system design and practical implementation for one or two specific applications, including design guidelines, system configuration, optical design, component requirements and selection, system optimization and design examples, recent advances and applications in biomedical researches and clinical imaging. This book serves as a reference for students and researchers in optics and biomedical engineering.
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
This book provides a state-of-the-art overview of the combined use of imaging modalities to obtain important functional and morphological information on intravascular disease and enhance disease detection. It discusses the integration of intravascular ultrasound (IVUS, intravascular optical coherence tomography (OCT), intravascular photoacoustic imaging (IVPA) and acoustic radiation force optical coherence elastography (ARF-OCE), and introduces the integration of multimodality imaging systems, such as IR and florescence. It includes the latest research advances and numerous imaging photos to offer readers insights into current intravascular applications. It is a valuable resource for students, scientists and physicians wanting to gain a deeper understanding of multimodality imaging tools.
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.