Описание: Invited Papers.- Glioma Diagnosis and Classification: Illuminating the Gold Standard.- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods.- Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics.- Brain Lesion Image Analysis.- Automatic Segmentation of Non-Tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks.- Convolutional neural network with asymmetric encoding and decoding structure for brain vessel segmentation on computed tomographic angiography.- Volume Preserving Brain Lesion Segmentation.- Microstructural modulations in the hippocampus allow to characterizing relapsing-remitting versus primary progressive multiple sclerosis.- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology.- Multivariate analysis is sufficient for lesion-behaviour mapping.- Label-Efficient Multi-Task Segmentation using Contrastive Learning.- Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation.- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection.- Unsupervised 3D Brain Anomaly Detection.- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI Tejas Sudharshan Mathai, Yi Wang, Nathan Cross.- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions.- Brain Tumor Segmentation.- Brain Tumor Segmentation Using Dual-Path Attention U-net in 3D MRI Images.- Multimodal Brain Image Analysis and Survival Prediction.- Using Neuromorphic Attention-based Neural Networks.- Context Aware 3D UNet for Brain Tumor Segmentation.- Modality-Pairing Learning for Brain Tumor Segmentation.- Transfer Learning for Brain Tumor Segmentation.- Efficient embedding network for 3D brain tumor segmentation.- Segmentation of the multimodal brain tumor images used Res-U-Net.- Vox2Vox: 3D-GAN for Brain Tumour Segmentation.- Automatic Brain Tumor Segmentation with Scale Attention Network.- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction.- Overall Survival Prediction for Glioblastoma on Pre-Treatment MRI Using Robust Radiomics and Priors.- Glioma segmentation using encoder-decoder network and survival prediction based on cox analysis.- Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution.- Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images.- MRI brain tumor segmentation using a 2D-3D U-Net ensemble.- Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-Ensemble ResUNet.- MRI Brain Tumor Segmentation and Uncertainty Estimation using 3D-UNet architectures.- Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival Prediction.- Uncertainty-driven refinement of tumor core segmentation using 3D-to-2D networks with label uncertainty.- Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation.- MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking.- A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation.- Ensemble of Two Dimensional Networks for Bain Tumor Segmentation.- Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation.- Low-Rank Convolutional Networks for Brain Tumor Segmentation.- Brain tumour segmentation using cascaded 3D densely-connected U-net.- Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction.- Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network.- Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided S
Автор: Richard A. Rudick; Donald E. Goodkin Название: Treatment of Multiple Sclerosis ISBN: 144713186X ISBN-13(EAN): 9781447131861 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Over the past decade, we have made great advances in the field of multiple sclerosis (MS) research, and this book focuses on those advances in MS pathogenesis and treatment. While some of these advances have been through new approaches and ideas that have emerged in the last decade such as the newly identified protective role that amyloid proteins may play in MS or the use of helminths to treat autoimmune diseases, others have evolved from previous theories and ideas that have only now gained momentum and a deeper understanding such as the role of HLA or gender in MS susceptibility. This book covers these emerging and evolving topics and highlights the substantial advancements made in elucidation of the factors regulating susceptibility or disease progression, identification of new ways to monitor or predict MS pathology, and development of new strategies for treating MS.
Автор: Alexzander A. A. Asea; Fabiana Geraci; Punit Kaur Название: Multiple Sclerosis: Bench to Bedside ISBN: 3319838431 ISBN-13(EAN): 9783319838434 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Нет в наличии.
Описание: Multiple Sclerosis (MS) is one of the main causes of disability in young adult population. Multiple Sclerosis: Bench to Bedside provides the most up-to-date and concise reviews on the critical issues of multiple sclerosis from around the world.
Автор: Seung U. Kim Название: Myelination and Demyelination ISBN: 146128077X ISBN-13(EAN): 9781461280774 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In June 1987, neurobiologists, immunologists, molecular biologists, virologists and neurologists from several countries met in Vancouver to discuss recent advances of relevance to multiple sclerosis.
Описание: Brain Tumor Segmentation.- Lightweight U-Nets for Brain Tumor Segmentation.- Efficient Brain Tumour Segmentation using Co-registered Data and Ensembles of Specialised Learners.- Efficient MRI Brain Tumor Segmentation using Multi-Resolution Encoder-Decoder Networks.- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework.- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation.- H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task.- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation.- Attention U-Net with Dimension-hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation.- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation.- Glioma Segmentation with 3D U-Net Backed with Energy- Based Post- Processing.- nnU-Net for Brain Tumor Segmentation.- A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation.- Brain tumor segmentation and associated uncertainty evaluation using Multi-sequences MRI Mixture Data Preprocessing.- A Deep supervision CNN network for Brain tumor Segmentation.- Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans.- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation.- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion.- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge.- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction.- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI using Selective Kernel Networks.- 3D brain tumor segmentation and survival prediction using ensembles of Convolutional Neural Networks.- Brain Tumour Segmentation using Probabilistic U-Net.- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets.- A Deep Supervised U-Attention Net for Pixel-wise Brain Tumor Segmentation.- A two stage atrous convolution neural network for brain tumor segmentation.- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI data.- Brain Tumor Segmentation and Survival Prediction using Automatic Hardmining in 3D CNN Architecture.- Some New Tricks for Deep Glioma Segmentation.- PieceNet: A Redundant UNet Ensemble.- Cerberus: A Multi-headed Network for BrainTumor Segmentation.- An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients based on Volumetric and Shape Features.- Squeeze-and-Excitation Normalization for Brain Tumor Segmentation.- Modified MobileNet for Patient Survival Prediction.- Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation.- Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified U-Net.- DR-Unet104 for Multimodal MRI brain tumor segmentation.- Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout.- Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation.- Learning Dynamic Convolutions for Multi-Modal 3D MRI Brain Tumor Segmentation.- Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification.- Automatic Glioma Grading Based on Two-stage Networks by Integrating Pathology and MRI Images.- Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images.- Multimodal brain tumor classification.- A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSI.- CNN-based Fully Automatic Glioma Classification with Multi-modal Medical Images.- Glioma Classification Using Multimodal Radiology and Histology Data.
Описание: This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES, MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Granada, Spain, in September 2018. The 92 papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges.
Автор: Paolo Livrea; E.J. Thompson; Maria Trojano Название: Cerebrospinal Fluid Analysis in Multiple Sclerosis ISBN: 884702207X ISBN-13(EAN): 9788847022072 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the spring of 1993 a meeting of the European Charcot Foundation was held in Bari as it was quite clear that there was a renaissance of Italian interest in cerebro- spinal fluid.
Автор: Ian D. Duncan; Robin J M Franklin Название: Myelin Repair and Neuroprotection in Multiple Sclerosis ISBN: 1489988408 ISBN-13(EAN): 9781489988409 Издательство: Springer Рейтинг: Цена: 22201.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers the novel approach of myelin repair to restore conduction to demyelinated fibers, and to protect them against future loss, now emerging as a translational therapy for MS. Experts discuss what is needed to move toward clinical applications.
Описание: This monograph approaches from various angles the problematic difficulties of definition of multiple sclerosis. Although no figures or tables are included, a schematic series of attempts are made in terms of short review style the overall and specific highlights of pathologic involvement of the central nervous system in neurodegeneration, neuroinflammation and demyelination as seen in autopsy material. A clear concise style has been adopted that allows however the establishment of comprehensive inclusion of various pathogenic features of operative schematization of current understanding in MS pathogenesis. This volume is particularly suitable in view of the organic structuring of various operative factors in MS that interest students, post-graduates researchers of various disciplines, and also integrative information of interest to clinicians in active clinical investigations, and also pathologists and neuropathologists.
Автор: Penner Название: Fatigue in Multiple Sclerosis ISBN: 3031134974 ISBN-13(EAN): 9783031134975 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Нет в наличии.
Описание: Fatigue is a major symptom in patients with multiple sclerosis (pwMS) and is mainly responsible for unemployment, early retirement but also social withdrawal. This book combines reporting of actually existing scientific knowledge with guidance for clinical practice. As such the book helps health care professionals in all countries to better understand fatigue symptoms but also to help patients to better cope with them.
Автор: M. Filippi; M. Rovaris; G. Comi Название: Neurodegeneration in Multiple Sclerosis ISBN: 8847055830 ISBN-13(EAN): 9788847055834 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written by world-renowned scientists, the volume provides a state-of-the-art on the most recent MRI techniques related to MS, and it is an indispensable tool for all those working in this field.
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