Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Record no. 177331)

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control field 978-3-031-09002-8
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control field 20240423125400.0
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031090028
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024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-031-09002-8
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1634
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQV
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Subject category code COM016000
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Subject category code UYQV
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.37
Edition number 23
245 10 - TITLE STATEMENT
Title Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Medium [electronic resource] :
Remainder of title 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II /
Statement of responsibility, etc edited by Alessandro Crimi, Spyridon Bakas.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2022.
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2022.
300 ## - PHYSICAL DESCRIPTION
Extent XXIII, 601 p. 225 illus., 195 illus. in color.
Other physical details online resource.
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490 1# - SERIES STATEMENT
Series statement Lecture Notes in Computer Science,
International Standard Serial Number 1611-3349 ;
Volume number/sequential designation 12963
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation -- Optimized U-Net for Brain Tumor Segmentation -- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation -- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database -- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation -- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation -- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks -- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI -- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation -- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging -- Disparity Autoencoders for Multi-class Brain Tumor Segmentation -- Disparity Autoencoders for Multi-class Brain Tumor Segmentation -- Disparity Autoencoders for Multi-class BrainTumor Segmentation -- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution -- Quality-aware Model Ensemble for Brain Tumor Segmentation -- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs -- An Ensemble Approach to Automatic Brain Tumor Segmentation -- Extending nn-UNet for brain tumor segmentation -- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge -- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI -- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation -- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features -- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation -- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.
520 ## - SUMMARY, ETC.
Summary, etc This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer vision.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medical informatics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Social sciences
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Application software.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Education
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Vision.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Health Informatics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Application in Social and Behavioral Sciences.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer and Information Systems Applications.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computers and Education.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Crimi, Alessandro.
Relator term editor.
Relator code edt
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Bakas, Spyridon.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
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Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031090011
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031090035
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Lecture Notes in Computer Science,
-- 1611-3349 ;
Volume number/sequential designation 12963
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-09002-8">https://doi.org/10.1007/978-3-031-09002-8</a>
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Koha item type eBooks-CSE-Springer

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