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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Record no. 86023)

000 -LEADER
fixed length control field 07202nam a22005775i 4500
001 - CONTROL NUMBER
control field 978-3-030-72087-2
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164955.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 210325s2021 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030720872
-- 978-3-030-72087-2
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-030-72087-2
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
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM016000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
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 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II /
Statement of responsibility, etc. edited by Alessandro Crimi, Spyridon Bakas.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer International Publishing :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2021.
300 ## - PHYSICAL DESCRIPTION
Extent XIX, 523 p. 25 illus.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement Image Processing, Computer Vision, Pattern Recognition, and Graphics,
International Standard Serial Number 3004-9954 ;
Volume/sequential designation 12659
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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 forMultimodal 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.
520 ## - SUMMARY, ETC.
Summary, etc. This 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer vision.
9 (RLIN) 86906
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
9 (RLIN) 1831
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Pattern recognition systems.
9 (RLIN) 3953
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Bioinformatics.
9 (RLIN) 9561
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer Vision.
9 (RLIN) 86908
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning.
9 (RLIN) 1831
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Automated Pattern Recognition.
9 (RLIN) 31568
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computational and Systems Biology.
9 (RLIN) 31619
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Crimi, Alessandro.
Relator term editor.
Relationship edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 86910
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Bakas, Spyridon.
Relator term editor.
Relationship edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 86912
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
9 (RLIN) 86914
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783030720865
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783030720889
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Image Processing, Computer Vision, Pattern Recognition, and Graphics,
International Standard Serial Number 3004-9954 ;
Volume/sequential designation 12659
9 (RLIN) 86915
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-030-72087-2">https://doi.org/10.1007/978-3-030-72087-2</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-Lecture Notes in CS

No items available.