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: Huazhu Fu; Mona K. Garvin; Tom MacGillivray; Yanwu Название: Ophthalmic Medical Image Analysis ISBN: 3030329550 ISBN-13(EAN): 9783030329556 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Dictionary Learning Informed Deep Neural Network with Application to OCT Images.- Structure-aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Image.- Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography.- An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans.- Foveal avascular zone segmentation in clinical routine fluorescein angiographies using multitask learning.- Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries.- 3D-CNN for Glaucoma Detection using Optical Coherence Tomography.- Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening.- Shape Decomposition of Foveal Pit Morphology using Scan Geometry Corrected OCT.- U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography.- Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images.- Robust Optic Disc Localization by Large Scale Learning.- The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detections.- Fundus Image based Retinal Vessel Segmentation Utilizing A Fast and Accurate Fully Convolutional Network.- Network pruning for OCT image classification.- An improved MPB-CNN segmentation method for edema area and neurosensory retinal detachment in SD-OCT images.- Encoder-Decoder Attention Network for Lesion Segmentation of Diabetic Retinopathy.- Multi-Discriminator Generative Adversarial Networks for improved thin retinal vessel segmentation.- Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior.- Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network.- Automated Stage Analysis of Retinopathy of Prematurity Using Joint Segmentation and Multi-Instance Learning.- Retinopathy Diagnosis using Semi-supervised Multi-channel Generative Adversarial Network.
Автор: Yang Название: Medical Image Understanding and Analysis ISBN: 3031120523 ISBN-13(EAN): 9783031120527 Издательство: Springer Рейтинг: Цена: 13415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 26th Conference on Medical Image Understanding and Analysis, MIUA 2022, held in Cambridge, UK, in July 2022.
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;
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.
Автор: Paulsen Rasmus R., Moeslund Thomas B. Название: Introduction to Medical Image Analysis ISBN: 3030393631 ISBN-13(EAN): 9783030393632 Издательство: Springer Рейтинг: Цена: 5487.00 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;
Автор: Maria A. Zuluaga; Kanwal Bhatia; Bernhard Kainz; M Название: Reconstruction, Segmentation, and Analysis of Medical Images ISBN: 3319522795 ISBN-13(EAN): 9783319522791 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Registration.- Reconstruction.- Deep learning for heart segmentation.- Discrete optimization and probabilistic intensity modeling.- Atlas-based strategies.- Random forests.
Автор: El-Baz, Ayman ; Mohammad a, Ghazal ; Suri, Jasjit Название: Handbook of Texture Analysis: Ai-Based Medical Imaging Applications ISBN: 0367483459 ISBN-13(EAN): 9780367483456 Издательство: Taylor&Francis Рейтинг: Цена: 25265.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Zheng Название: Marginal Space Learning for Medical Image Analysis ISBN: 1493905996 ISBN-13(EAN): 9781493905997 Издательство: Springer Рейтинг: Цена: 9756.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications.
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;
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 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.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
