Título :	Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I
Tipo de documento:	documento electrónico
Autores:	Crimi, Alessandro, ; Bakas, Spyridon,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2021
Número de páginas:	XX, 529 p. 197 ilustraciones, 180 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-72084-1
Nota general:	Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave:	Visión por computador  Aprendizaje automático  Sistemas de reconocimiento de patrones  Bioinformática  Reconocimiento de patrones automatizado  Biología Computacional y de Sistemas
Índice Dewey:	006.37 Visión artificial
Resumen:	Este conjunto de dos volúmenes LNCS 12658 y 12659 constituye las actas minuciosamente arbitradas del 6.º Taller internacional sobre lesiones cerebrales MICCAI, BrainLes 2020, el desafío internacional de segmentación multimodal de tumores cerebrales (BraTS) y el desafío de medicina computacional de precisión: desafío de radiología-patología en la clasificación de tumores cerebrales. (CPM-RadPath) desafío. Estos se llevaron a cabo conjuntamente en la 23.ª Conferencia sobre Computación de Imágenes Médicas para Intervenciones Asistidas por Computadora, MICCAI 2020, en Lima, Perú, en octubre de 2020.* Los artículos seleccionados revisados ​​presentados en estos volúmenes se organizaron en las siguientes secciones temáticas: análisis de imágenes de lesiones cerebrales ( 16 artículos seleccionados de 21 presentaciones); segmentación de imágenes de tumores cerebrales (69 artículos seleccionados de 75 presentaciones); y medicina de precisión computacional: desafío de radiología-patología en la clasificación de tumores cerebrales (6 artículos seleccionados de 6 presentaciones). *El taller y desafíos se realizaron de manera virtual.
Nota de contenido:	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 Survival Prediction.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I [documento electrónico] / Crimi, Alessandro, ; Bakas, Spyridon,  . -  1 ed.  . - [s.l.] : Springer, 2021 . - XX, 529 p. 197 ilustraciones, 180 ilustraciones en color.
ISBN : 978-3-030-72084-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.

Palabras clave:	Visión por computador  Aprendizaje automático  Sistemas de reconocimiento de patrones  Bioinformática  Reconocimiento de patrones automatizado  Biología Computacional y de Sistemas
Índice Dewey:	006.37 Visión artificial
Resumen:	Este conjunto de dos volúmenes LNCS 12658 y 12659 constituye las actas minuciosamente arbitradas del 6.º Taller internacional sobre lesiones cerebrales MICCAI, BrainLes 2020, el desafío internacional de segmentación multimodal de tumores cerebrales (BraTS) y el desafío de medicina computacional de precisión: desafío de radiología-patología en la clasificación de tumores cerebrales. (CPM-RadPath) desafío. Estos se llevaron a cabo conjuntamente en la 23.ª Conferencia sobre Computación de Imágenes Médicas para Intervenciones Asistidas por Computadora, MICCAI 2020, en Lima, Perú, en octubre de 2020.* Los artículos seleccionados revisados ​​presentados en estos volúmenes se organizaron en las siguientes secciones temáticas: análisis de imágenes de lesiones cerebrales ( 16 artículos seleccionados de 21 presentaciones); segmentación de imágenes de tumores cerebrales (69 artículos seleccionados de 75 presentaciones); y medicina de precisión computacional: desafío de radiología-patología en la clasificación de tumores cerebrales (6 artículos seleccionados de 6 presentaciones). *El taller y desafíos se realizaron de manera virtual.
Nota de contenido:	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 Survival Prediction.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i