Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, Unsur, Sudre Carole H., Fehri Hamid, Arbel Tal
Автор: Hayit Greenspan; Ryutaro Tanno; Marius Erdt; Tal A Название: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures ISBN: 3030326888 ISBN-13(EAN): 9783030326883 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Danail Stoyanov; Zeike Taylor; Enzo Ferrante; Adri Название: Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities ISBN: 3030006883 ISBN-13(EAN): 9783030006884 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 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 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets
Автор: M. Emre Celebi, Teresa Mendonca, Jorge S. Marques Название: Dermoscopy Image Analysis ISBN: 1138892874 ISBN-13(EAN): 9781138892873 Издательство: Taylor&Francis Рейтинг: Цена: 47970.00 T Наличие на складе: Невозможна поставка. Описание: Dermoscopy is a noninvasive skin imaging technique that uses optical magnification and either liquid immersion or cross-polarized lighting to make subsurface structures more easily visible when compared to conventional clinical images. It allows for the identification of dozens of morphological features that are particularly important in identifying malignant melanoma. Dermoscopy Image Analysis summarizes the state of the art of the computerized analysis of dermoscopy images. The book begins by discussing the influence of color normalization on classification accuracy and then: Investigates gray-world, max-RGB, and shades-of-gray color constancy algorithms, showing significant gains in sensitivity and specificity on a heterogeneous set of images Proposes a new color space that highlights the distribution of underlying melanin and hemoglobin color pigments, leading to more accurate classification and border detection results Determines that the latest border detection algorithms can achieve a level of agreement that is only slightly lower than the level of agreement among experienced dermatologists Provides a comprehensive review of various methods for border detection, pigment network extraction, global pattern extraction, streak detection, and perceptually significant color detection Details a computer-aided diagnosis (CAD) system for melanomas that features an inexpensive acquisition tool, clinically meaningful features, and interpretable classification feedback Presents a highly scalable CAD system implemented in the MapReduce framework, a novel CAD system for melanomas, and an overview of dermatological image databases Describes projects that made use of a publicly available database of dermoscopy images, which contains 200 high-quality images along with their medical annotations Dermoscopy Image Analysis not only showcases recent advances but also explores future directions for this exciting subfield of medical image analysis, covering dermoscopy image analysis from preprocessing to classification.
Автор: M. Jorge Cardoso; Tal Arbel; Enzo Ferrante; Xavier Название: Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics ISBN: 3319676741 ISBN-13(EAN): 9783319676746 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.
The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.
Автор: Jiri Jan Название: Medical Image Processing, Reconstruction and Analysis: Concepts and Methods, Second Edition ISBN: 113831028X ISBN-13(EAN): 9781138310285 Издательство: Taylor&Francis Рейтинг: Цена: 224570.00 T Наличие на складе: Невозможна поставка. Описание: Medical Image Processing, Reconstruction and Analysis – Concepts and Methods explains the general principles and methods of image processing, focusing namely on applications used in medical imaging – providing a theoretical yet clear and easy to follow explanation of underlying generic concepts. The content of this book is divided into three parts: Part I – Images as Multidimensional Signals provides the introduction tobasic image processing theory, explaining it for both analogue and digital image representations. Part II – Imaging Systems as Data Sources offers a non-traditional view on imaging modalities, explaining – without technical details – their basic principles influencing the properties of the obtained images, with emphasis placed on analyzing the internal signals and (pre)image data that are to be processed by the methods described in this book. Part III – Image Processing and Analysis focuses on such vital image processing topics as tomographic image reconstruction, image fusion, methods if image enhancement and restoration. It explains concepts of both fundamental-level image analysis detailing local feature, edge and texture analysis, image segmentation and morphological transforms, and higher-level analysis, as principal and independent component analysis and namely the new analysis area based on deep learning, namely that using convolutional neural networks. Briefly, also the medical image-processing environment is briefly treated, including the processes for image archiving and communication. Features Presents a good, theoretically exact yet understandable overview of basic theory related to image processing and analysis, with practical interpretations of all theoretical conclusions Provides a concise treatment of a wide variety of medical imaging modalities with respect to properties of image data to be processed Includes topical discussions on medical image reconstruction, fusion, enhancement and restoration as well as on image analysis including the recently appearing deep-learning based methods Explores appropriate applications relevant to particular chapters
Название: Image Processing and Analysis with Graphs ISBN: 1138071765 ISBN-13(EAN): 9781138071766 Издательство: Taylor&Francis Рейтинг: Цена: 86760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Covering the theoretical aspects of image processing and analysis through the use of graphs in the representation and analysis of objects, Image Processing and Analysis with Graphs: Theory and Practice also demonstrates how these concepts are indispensible for the design of cutting-edge solutions for real-world applications.
Explores new applications in computational photography, image and video processing, computer graphics, recognition, medical and biomedical imaging
With the explosive growth in image production, in everything from digital photographs to medical scans, there has been a drastic increase in the number of applications based on digital images. This book explores how graphs--which are suitable to represent any discrete data by modeling neighborhood relationships--have emerged as the perfect unified tool to represent, process, and analyze images. It also explains why graphs are ideal for defining graph-theoretical algorithms that enable the processing of functions, making it possible to draw on the rich literature of combinatorial optimization to produce highly efficient solutions.
Some key subjects covered in the book include:
Definition of graph-theoretical algorithms that enable denoising and image enhancement
Energy minimization and modeling of pixel-labeling problems with graph cuts and Markov Random Fields
Image processing with graphs: targeted segmentation, partial differential equations, mathematical morphology, and wavelets
Analysis of the similarity between objects with graph matching
Adaptation and use of graph-theoretical algorithms for specific imaging applications in computational photography, computer vision, and medical and biomedical imaging
Use of graphs has become very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Accounting for the wide variety of problems being solved with graphs in image processing and computer vision, this book is a contributed volume of chapters written by renowned experts who address specific techniques or applications. This state-of-the-art overview provides application examples that illustrate practical application of theoretical algorithms. Useful as a support for graduate courses in image processing and computer vision, it is also perfect as a reference for practicing engineers working on development and implementation of image processing and analysis algorithms.
Автор: GRULL & KEBSCHULL Название: Acceleration of Biomedical Image Processing with Dataflow on FPGAs ISBN: 8793379366 ISBN-13(EAN): 9788793379367 Издательство: Taylor&Francis Рейтинг: Цена: 63280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Short compute times are crucial for timely diagnostics in biomedical applications, but lead to a high demand in computing for new and improved imaging techniques. In this book, reconfigurable computing with FPGAs is discussed as an alternative to multi-core processing and graphics card accelerators. Instead of adjusting the application to the hardware, FPGAs allow the hardware to also be adjusted to the problem. Acceleration of Biomedical Image Processing with Dataflow on FPGAs covers the transformation of image processing algorithms towards a system of deep pipelines that can be executed with very high parallelism. The transformation process is discussed from initial design decisions to working implementations. Two example applications from stochastic localization microscopy and electron tomography illustrate the approach further.
Topics discussed in the book include: - Reconfigurable hardware - Dataflow computing - Image processing - Application acceleration
Автор: Liu Mingxia, Yan Pingkun, Lian Chunfeng Название: Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 4, 2020, Procee ISBN: 3030598608 ISBN-13(EAN): 9783030598600 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI.- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion.- A Novel fMRI Representation Learning Framework with GAN.- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement.- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies.- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network.- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration.- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy.- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows.- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest.- A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation.- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network.- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior.- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation.- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes.- Anatomy-Aware Cardiac Motion Estimation.- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation.- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI.- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.- Boundary-aware Network for Kidney Tumor Segmentation.- O-Net: An Overall Convolutional Network for Segmentation Tasks.- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints.- EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis.- Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation.- Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer.- Exploring Functional Difference between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks.- Detection of Ischemic Infarct Core in Non-Contrast Computed Tomography.- Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers.- Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.- Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet.- Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification.- Multi-tasking Siamese Networks for Breast Mass Detection using Dual-view Mammogram Matching.- 3D Volume Reconstruction from Single Lateral X-ray Image via Cross-Modal Discrete Embedding Transition.- Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks.- A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.- Learning Conditional Deformable Shape Templates for Brain Anatomy .- Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.- Unsupervised Learning for Spherical Surface Registration.- Anatomy-guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI.- Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization.- Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors.- Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening .- Importance Driven Continual Learning for Segmentation Across Domains.- RDCNet: Instance segmentation with a minimalist recurrent residual network.- Automatic Segmentation of Achilles Tend
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: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Bhattacharyya Siddhartha, Konar Debanjan, Platos Jan Название: Hybrid Machine Intelligence for Medical Image Analysis ISBN: 9811389322 ISBN-13(EAN): 9789811389320 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book discusses the impact of machine learning and computational intelligent algorithms on medical image data processing, and introduces the latest trends in machine learning technologies and computational intelligence for intelligent medical image analysis.
Автор: K. Kamalanand, B. Thayumanavan, P. Mannar Jawahar Название: Computational Techniques for Dental Image Analysis ISBN: 1522562435 ISBN-13(EAN): 9781522562436 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 236010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With the technology innovations dentistry has witnessed in all its branches over the past three decades, the need for more precise diagnostic tools and advanced imaging methods has become mandatory across the industry. Recent advancements to imaging systems are playing an important role in efficient diagnoses, treatments, and surgeries.Computational Techniques for Dental Image Analysis provides innovative insights into computerized methods for automated analysis. The research presented within this publication explores pattern recognition, oral pathologies, and diagnostic processing. It is designed for dentists, professionals, medical educators, medical imaging technicians, researchers, oral surgeons, and students, and covers topics centered on easier assessment of complex cranio-facial tissues and the accurate diagnosis of various lesions at early stages.
