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Medical Image Understanding and Analysis, Waiter


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Автор: Waiter
Название:  Medical Image Understanding and Analysis
ISBN: 9783031485923
Издательство: Springer
Классификация:



ISBN-10: 3031485920
Обложка/Формат: Soft cover
Страницы: 340
Вес: 0.00 кг.
Дата издания: 16.12.2023
Серия: Lecture notes in computer science
Язык: English
Издание: 1st ed. 2024
Иллюстрации: 108 illustrations, color; 17 illustrations, black and white; xi, 340 p. 125 illus., 108 illus. in color.
Размер: 235 x 155
Основная тема: Computer Science
Подзаголовок: 27th annual conference, miua 2023, aberdeen, uk, july 19вђ“21, 2023, proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book constitutes the proceedings of the 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023, which took place in Aberdeen, UK, during July 19–21, 2023. The 24 full papers presented in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Image interpretation; radiomics, predictive models and quantitative imaging; image classification; and biomarker detection.
Дополнительное описание: Segmentation of White Matter Hyperintensities and Ischaemic Stroke Lesions in Structural MRI.- A Deep Learning Based Approach to Semantic Segmentation of Lung Tumour Areas in Gross Pathology Images.- Iterative Refinement Algorithm for Liver Segmentation G



Medical Image Understanding and Analysis: 23rd Conference, Miua 2019, Liverpool, Uk, July 24-26, 2019, Proceedings

Автор: Zheng Yalin, Williams Bryan M., Chen Ke
Название: Medical Image Understanding and Analysis: 23rd Conference, Miua 2019, Liverpool, Uk, July 24-26, 2019, Proceedings
ISBN: 3030393429 ISBN-13(EAN): 9783030393427
Издательство: Springer
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Цена: 6986.00 р.
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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;

Medical Image Understanding and Analysis: 25th Annual Conference, Miua 2021, Oxford, United Kingdom, July 12-14, 2021, Proceedings

Автор: Papież Bartlomiej W., Yaqub Mohammad, Jiao Jianbo
Название: Medical Image Understanding and Analysis: 25th Annual Conference, Miua 2021, Oxford, United Kingdom, July 12-14, 2021, Proceedings
ISBN: 3030804313 ISBN-13(EAN): 9783030804312
Издательство: Springer
Цена: 6986.00 р.
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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;

Advanced Machine Vision Paradigms For Medical Image Analysis

Автор: Gandhi, Tapan K.
Название: Advanced Machine Vision Paradigms For Medical Image Analysis
ISBN: 012819295X ISBN-13(EAN): 9780128192955
Издательство: Elsevier Science
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Цена: 19875.00 р.
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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.

Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Автор: Danail Stoyanov; Zeike Taylor; Seyed Mostafa Kia;
Название: Understanding and Interpreting Machine Learning in Medical Image Computing Applications
ISBN: 3030026272 ISBN-13(EAN): 9783030026271
Издательство: Springer
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Цена: 6986.00 р.
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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.

Hybrid Computational Intelligence

Автор: Bhattacharyya, Siddhartha
Название: Hybrid Computational Intelligence
ISBN: 0128186992 ISBN-13(EAN): 9780128186992
Издательство: Elsevier Science
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Цена: 19875.00 р.
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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.

Riemannian Geometric Statistics in Medical Image Analysis

Автор: Pennec Xavier
Название: Riemannian Geometric Statistics in Medical Image Analysis
ISBN: 0128147253 ISBN-13(EAN): 9780128147252
Издательство: Elsevier Science
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Цена: 16161.00 р.
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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
Introduction to Medical Image Analysis

Автор: Paulsen Rasmus R., Moeslund Thomas B.
Название: Introduction to Medical Image Analysis
ISBN: 3030393631 ISBN-13(EAN): 9783030393632
Издательство: Springer
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Цена: 6288.00 р.
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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;

Ophthalmic Medical Image Analysis: 7th International Workshop, Omia 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 8, 2020, Proceedin

Автор: Fu Huazhu, Garvin Mona K., Macgillivray Tom
Название: Ophthalmic Medical Image Analysis: 7th International Workshop, Omia 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 8, 2020, Proceedin
ISBN: 3030634183 ISBN-13(EAN): 9783030634186
Издательство: Springer
Цена: 6986.00 р.
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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.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, Unsur

Автор: Sudre Carole H., Fehri Hamid, Arbel Tal
Название: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, Unsur
ISBN: 3030603644 ISBN-13(EAN): 9783030603649
Издательство: Springer
Цена: 8104.00 р.
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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.

Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th Internat

Автор: Reyes Mauricio, Henriques Abreu Pedro, Cardoso Jaime
Название: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th Internat
ISBN: 3030874435 ISBN-13(EAN): 9783030874438
Издательство: Springer
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Цена: 7685.00 р.
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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.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis: 3rd Internationa

Автор: Sudre Carole H., Licandro Roxane, Baumgartner Christian
Название: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis: 3rd Internationa
ISBN: 3030877345 ISBN-13(EAN): 9783030877347
Издательство: Springer
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Цена: 9083.00 р.
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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.

Deep network design for medical image computing

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
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Цена: 14308.00 р.
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