國立虎尾科技大學 |

Head and Neck Tumor Segmentation and Outcome Prediction = Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Head and Neck Tumor Segmentation and Outcome Prediction/ edited by Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge.
其他題名:	Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
其他作者:	Depeursinge, Adrien.
面頁冊數:	X, 328 p. 102 illus., 88 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-98253-9
ISBN:	9783030982539

Head and Neck Tumor Segmentation and Outcome Prediction = Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
Head and Neck Tumor Segmentation and Outcome PredictionSecond Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /[electronic resource] :edited by Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge. - 1st ed. 2022. - X, 328 p. 102 illus., 88 illus. in color.online resource. - Lecture Notes in Computer Science,132091611-3349 ;. - Lecture Notes in Computer Science,9324.

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic -- Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images -- CCUT-Net: Pixel-wise Global Context Channel Attention UT-Net for head and neck tumor segmentation -- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images -- Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT images using 3D-Inception-ResNet Model -- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network -- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images -- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT -- The Head and Neck Tumor Segmentation based on 3D U-Net: 3D U-net applied to Simple Attention Module for Head and Neck tumor segmentation in PET and CT images -- Skip-SCSE Multi-Scale Attention and Co-Learning method for Oropharyngeal Tumor Segmentation on multi-modal PET-CT images -- Head and Neck Cancer Primary Tumor Auto Segmentation using Model Ensembling of Deep Learning in PET/CT Images -- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation -- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model -- Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients -- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer -- PET/CT Head and Neck tumor segmentation and Progression Free Survival prediction using Deep and Machine learning techniques -- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT images -- Multimodal PET/CT Tumour Segmentation and Progression-Free Survival Prediction using a Full-scale UNet with Attention -- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer -- Fusion-Based head and neck Tumor Segmentation and Survival prediction using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems -- Head and Neck Primary Tumor Segmentation using Deep Neural Networks and Adaptive Ensembling -- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks -- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction -- Deep Supervoxel Segmentation Survival Anaylsis in Head and Neck Cancer Patients -- A Hybrid Radiomics Approach to Modeling Progression-free Survival in Head and Neck Cancers -- An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data -- Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET/CT Imaging Data -- Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma -- Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer -- Comparing deep learning and conventional machine learning for outcome prediction of head and neck cancer in PET/CT.

This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training. .

ISBN: 9783030982539

Standard No.: 10.1007/978-3-030-98253-9doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: TA1501-1820

Dewey Class. No.: 006

Head and Neck Tumor Segmentation and Outcome Prediction = Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
LDR:05670nam a22004215i 4500 001 1090327
003 DE-He213
005 20220312193507.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030982539 $9 978-3-030-98253-9
024 7 $a 10.1007/978-3-030-98253-9 $2 doi
035 $a 978-3-030-98253-9
050 4 $a TA1501-1820
050 4 $a TA1634
072 7 $a UYT $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYT $2 thema
082 0 4 $a 006 $2 23
245 1 0 $a Head and Neck Tumor Segmentation and Outcome Prediction $h [electronic resource] : $b Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / $c edited by Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a X, 328 p. 102 illus., 88 illus. in color. $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 Lecture Notes in Computer Science, $x 1611-3349 ; $v 13209
505 0 $a Overview of the HECKTOR Challenge at MICCAI 2021: Automatic -- Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images -- CCUT-Net: Pixel-wise Global Context Channel Attention UT-Net for head and neck tumor segmentation -- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images -- Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT images using 3D-Inception-ResNet Model -- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network -- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images -- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT -- The Head and Neck Tumor Segmentation based on 3D U-Net: 3D U-net applied to Simple Attention Module for Head and Neck tumor segmentation in PET and CT images -- Skip-SCSE Multi-Scale Attention and Co-Learning method for Oropharyngeal Tumor Segmentation on multi-modal PET-CT images -- Head and Neck Cancer Primary Tumor Auto Segmentation using Model Ensembling of Deep Learning in PET/CT Images -- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation -- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model -- Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients -- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer -- PET/CT Head and Neck tumor segmentation and Progression Free Survival prediction using Deep and Machine learning techniques -- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT images -- Multimodal PET/CT Tumour Segmentation and Progression-Free Survival Prediction using a Full-scale UNet with Attention -- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer -- Fusion-Based head and neck Tumor Segmentation and Survival prediction using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems -- Head and Neck Primary Tumor Segmentation using Deep Neural Networks and Adaptive Ensembling -- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks -- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction -- Deep Supervoxel Segmentation Survival Anaylsis in Head and Neck Cancer Patients -- A Hybrid Radiomics Approach to Modeling Progression-free Survival in Head and Neck Cancers -- An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data -- Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET/CT Imaging Data -- Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma -- Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer -- Comparing deep learning and conventional machine learning for outcome prediction of head and neck cancer in PET/CT.
520 $a This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training. .
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computer and Information Systems Applications. $3 1365732
650 2 4 $a Computers and Education. $3 669806
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 0 $a Machine learning. $3 561253
650 0 $a Application software. $3 528147
650 0 $a Education—Data processing. $3 1253610
650 0 $a Computer vision. $3 561800
650 0 $a Image processing—Digital techniques. $3 1365735
700 1 $a Depeursinge, Adrien. $e editor. $1 https://orcid.org/0000-0002-2362-0304 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1356514
700 1 $a Hatt, Mathieu. $e editor. $1 https://orcid.org/0000-0002-8938-8667 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1397727
700 1 $a Oreiller, Valentin. $e editor. $1 https://orcid.org/0000-0002-7794-6916 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1356513
700 1 $a Andrearczyk, Vincent. $e editor. $1 https://orcid.org/0000-0003-0793-5821 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1356512
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030982522
776 0 8 $i Printed edition: $z 9783030982546
830 0 $a Lecture Notes in Computer Science, $x 0302-9743 ; $v 9324 $3 1258960
856 4 0 $u https://doi.org/10.1007/978-3-030-98253-9
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)