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001435524 019__ $$a1253409547$$a1255891158$$a1281411603$$a1283903559$$a1284938746$$a1287272989$$a1287882687
001435524 020__ $$a9783030720841$$q(electronic book)
001435524 020__ $$a3030720845$$q(electronic book)
001435524 020__ $$a3030720837
001435524 020__ $$a9783030720834
001435524 020__ $$a9783030720858$$q(print)
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001435524 0247_ $$a10.1007/978-3-030-72084-1$$2doi
001435524 035__ $$aSP(OCoLC)1244629114
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001435524 049__ $$aISEA
001435524 050_4 $$aRC280.B7
001435524 08204 $$a616.8$$223
001435524 1112_ $$aBrainLes (Workshop)$$n(6th :$$d2020 :$$cOnline)
001435524 24510 $$aBrainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries :$$b6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers.$$nPart I /$$cAlessandro Crimi, Spyridon Bakas (eds.).
001435524 2463_ $$aBrainLes 2020
001435524 264_1 $$aCham :$$bSpringer International Publishing AG,$$c[2021]
001435524 264_4 $$c©2021
001435524 300__ $$a1 online resource (544 pages)
001435524 336__ $$atext$$btxt$$2rdacontent
001435524 337__ $$acomputer$$bc$$2rdamedia
001435524 338__ $$aonline resource$$bcr$$2rdacarrier
001435524 347__ $$atext file
001435524 347__ $$bPDF
001435524 4901_ $$aLecture notes in computer science ;$$v12658
001435524 4901_ $$aLNCS Sublibrary: SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics
001435524 504__ $$aIncludes bibliographical references and author index.
001435524 5050_ $$aInvited 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.
001435524 506__ $$aAccess limited to authorized users.
001435524 520__ $$aThis two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.
001435524 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 14, 2021).
001435524 650_0 $$aBrain$$xTumors$$vCongresses.
001435524 650_0 $$aBrain$$xWounds and injuries$$vCongresses.
001435524 650_0 $$aCerebrovascular disease$$vCongresses.
001435524 650_6 $$aCerveau$$xTumeurs$$vCongrès.
001435524 650_6 $$aCerveau$$xLésions et blessures$$vCongrès.
001435524 650_6 $$aAccidents vasculaires cérébraux$$vCongrès.
001435524 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001435524 655_7 $$aConference papers and proceedings.$$2lcgft
001435524 655_7 $$aActes de congrès.$$2rvmgf
001435524 655_0 $$aElectronic books.
001435524 7001_ $$aCrimi, Alessandro,$$eeditor.
001435524 7001_ $$aBakas, Spyridon,$$eeditor.
001435524 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(23rd :$$d2020 :$$cOnline),$$jjointly held conference.
001435524 77608 $$iPrint version:$$z9783030720834
001435524 830_0 $$aLecture notes in computer science ;$$v12658.
001435524 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001435524 852__ $$bebk
001435524 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-72084-1$$zOnline Access$$91397441.1
001435524 909CO $$ooai:library.usi.edu:1435524$$pGLOBAL_SET
001435524 980__ $$aBIB
001435524 980__ $$aEBOOK
001435524 982__ $$aEbook
001435524 983__ $$aOnline
001435524 994__ $$a92$$bISE