國立虎尾科技大學 |

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 II /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries/ edited by Alessandro Crimi, Spyridon Bakas.
其他題名:	6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II /
其他作者:	Bakas, Spyridon.
面頁冊數:	XIX, 523 p. 25 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Biology/Bioinformatics. -
電子資源:	https://doi.org/10.1007/978-3-030-72087-2
ISBN:	9783030720872

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 II /
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II /[electronic resource] :edited by Alessandro Crimi, Spyridon Bakas. - 1st ed. 2021. - XIX, 523 p. 25 illus.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12659. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

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.

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.

ISBN: 9783030720872

Standard No.: 10.1007/978-3-030-72087-2doiSubjects--Topical Terms:

677363
Computational Biology/Bioinformatics.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

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 II /
LDR:06480nam a22004335i 4500 001 1046037
003 DE-He213
005 20210826135516.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030720872 $9 978-3-030-72087-2
024 7 $a 10.1007/978-3-030-72087-2 $2 doi
035 $a 978-3-030-72087-2
050 4 $a TA1630-1650
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
082 0 4 $a 006.37 $2 23
245 1 0 $a Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries $h [electronic resource] : $b 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II / $c edited by Alessandro Crimi, Spyridon Bakas.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XIX, 523 p. 25 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 12659
505 0 $a 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.
520 $a 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 2 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Image Processing and Computer Vision. $3 670819
650 0 $a Bioinformatics. $3 583857
650 0 $a Pattern recognition. $3 1253525
650 0 $a Machine learning. $3 561253
650 0 $a Optical data processing. $3 639187
700 1 $a Bakas, Spyridon. $e editor. $1 https://orcid.org/0000-0001-8734-6482 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283981
700 1 $a Crimi, Alessandro. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1106621
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030720865
776 0 8 $i Printed edition: $z 9783030720889
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-72087-2
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)