Описание: This book constitutes the refereed proceedings of the 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, held in Liverpool, UK, in July 2019. The 43 full papers presented were carefully reviewed and selected from 70 submissions. There were organized in topical sections named: oncology and tumour imaging;
Описание: This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. image registration, and reconstruction; image enhancement, quality assessment, and data privacy;
Автор: Gandhi, Tapan K. Название: Advanced Machine Vision Paradigms For Medical Image Analysis ISBN: 012819295X ISBN-13(EAN): 9780128192955 Издательство: Elsevier Science Рейтинг: Цена: 19875.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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 unstructured nature of medical imaging data and the volume of data produced during routine clinical process, 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 can exploit 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 both 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 the already high medical costs.
Описание: This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 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 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
Автор: Bhattacharyya, Siddhartha Название: Hybrid Computational Intelligence ISBN: 0128186992 ISBN-13(EAN): 9780128186992 Издательство: Elsevier Science Рейтинг: Цена: 19875.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development.
Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.
Автор: Pennec Xavier Название: Riemannian Geometric Statistics in Medical Image Analysis ISBN: 0128147253 ISBN-13(EAN): 9780128147252 Издательство: Elsevier Science Рейтинг: Цена: 16161.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data.
Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods.
Beyond medical image computing, the methods described in this book may also apply to other domains such as signal processing, computer vision, geometric deep learning, and other domains where statistics on geometric features appear. As such, the presented core methodology takes its place in the field of geometric statistics, the statistical analysis of data being elements of nonlinear geometric spaces. The foundational material and the advanced techniques presented in the later parts of the book can be useful in domains outside medical imaging and present important applications of geometric statistics methodology
Content includes:
The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs
Applications of statistics on manifolds and shape spaces in medical image computing
Diffeomorphic deformations and their applications
As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science.
A complete reference covering both the foundations and state-of-the-art methods
Edited and authored by leading researchers in the field
Contains theory, examples, applications, and algorithms
Gives an overview of current research challenges and future applications
Автор: Paulsen Rasmus R., Moeslund Thomas B. Название: Introduction to Medical Image Analysis ISBN: 3030393631 ISBN-13(EAN): 9783030393632 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: reviews the basic image processing methods for segmenting or enhancing certain features in an image, with a focus on morphology methods for binary images; 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;
Описание: This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
Описание: 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.
Описание: iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool Management.- Soft Attention Improves Skin Cancer Classification Performance.- Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and Prognosis.- This explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.- Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions.- The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths for Persistent Diagrams.- Neighborhood complex based machine learning (NCML) models for drug design.- Predictive modelling of highly multiplexed tumour tissue images by graph neural networks.- Statistical modeling of pulmonary vasculatures with topological priors in CT volumes.- Topological Detection of Alzheimer's Disease using Betti Curves.
Описание: This book constitutes the refereed proceedings of the Third 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.
Автор: Liao, Haofu (applied Scientist, Rekognition And Video Analysis Team, Amazon Web Services, Inc, Ca, Usa) Zhou, S. Kevin (professor, Institute Of Comput Название: Deep network design for medical image computing ISBN: 012824383X ISBN-13(EAN): 9780128243831 Издательство: Elsevier Science Рейтинг: Цена: 14308.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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