001445131 000__ 06868cam\a2200601Ii\4500
001445131 001__ 1445131
001445131 003__ OCoLC
001445131 005__ 20230310003814.0
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001445131 020__ $$a9783030982539$$q(electronic bk.)
001445131 020__ $$a303098253X$$q(electronic bk.)
001445131 020__ $$z9783030982522$$q(print)
001445131 0247_ $$a10.1007/978-3-030-98253-9$$2doi
001445131 035__ $$aSP(OCoLC)1303570448
001445131 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dOCLCQ
001445131 049__ $$aISEA
001445131 050_4 $$aRC78.7.D53
001445131 08204 $$a616.07/54$$223
001445131 1112_ $$aHead and Neck Tumor Segmentation Challenge$$n(2nd :$$d2021 :$$cOnline)
001445131 24510 $$aHead and neck tumor segmentation and outcome prediction :$$bsecond challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /$$cVincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge (eds.).
001445131 2463_ $$aHECKTOR 2021
001445131 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001445131 300__ $$a1 online resource (x, 328 pages) :$$billustrations (some color).
001445131 336__ $$atext$$btxt$$2rdacontent
001445131 337__ $$acomputer$$bc$$2rdamedia
001445131 338__ $$aonline resource$$bcr$$2rdacarrier
001445131 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13209
001445131 500__ $$aIncludes author index.
001445131 5050_ $$aOverview 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.
001445131 506__ $$aAccess limited to authorized users.
001445131 520__ $$aThis book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training.
001445131 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 15, 2022).
001445131 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
001445131 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
001445131 650_0 $$aCancer$$xTreatment$$xTechnological innovations$$vCongresses.
001445131 650_6 $$aImagerie pour le diagnostic$$xInformatique$$vCongrès.
001445131 650_6 $$aIntelligence artificielle en médecine$$vCongrès.
001445131 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001445131 655_0 $$aElectronic books.
001445131 7001_ $$aAndrearczyk, Vincent,$$eeditor.$$1https://orcid.org/0000-0003-0793-5821
001445131 7001_ $$aOreiller, Valentin,$$eeditor.$$1https://orcid.org/0000-0002-7794-6916
001445131 7001_ $$aHatt, Mathieu,$$eeditor.$$0(orcid)0000-0002-8938-8667$$1https://orcid.org/0000-0002-8938-8667
001445131 7001_ $$aDepeursinge, Adrien,$$eeditor.$$1https://orcid.org/0000-0002-2362-0304
001445131 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(24th :$$d2021 :$$cOnline)
001445131 830_0 $$aLecture notes in computer science ;$$v13209.$$x1611-3349
001445131 852__ $$bebk
001445131 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-98253-9$$zOnline Access$$91397441.1
001445131 909CO $$ooai:library.usi.edu:1445131$$pGLOBAL_SET
001445131 980__ $$aBIB
001445131 980__ $$aEBOOK
001445131 982__ $$aEbook
001445131 983__ $$aOnline
001445131 994__ $$a92$$bISE