Head and Neck Tumor Segmentation and Outcome Prediction
portes grátis
Head and Neck Tumor Segmentation and Outcome Prediction
Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Oreiller, Valentin; Depeursinge, Adrien; Hatt, Mathieu; Andrearczyk, Vincent
Springer International Publishing AG
03/2023
257
Mole
Inglês
9783031274190
15 a 20 dias
Descrição não disponível.
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT 1.- Automated head and neck tumor segmentation from 3D PET/CTHECKTOR 2022 challenge report.- A Coarse-to-Fine Ensembling Framework for Head and Neck Tumorand Lymph Segmentation in CT and PET Images.- A General Web-based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images.- Octree Boundary Transfiner: Effcient Transformers for Tumor Segmentation Refinement.- Head and Neck Primary Tumor and Lymph Node Auto-Segmentationfor PET/CT Scans.- Fusion-based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques.- Stacking Feature Maps of Multi-Scaled Medical Images in U-Net for 3DHead and Neck Tumor Segmentation.- A fine-tuned 3D U-net for primary tumor and affected lymph nodessegmentationin fused multimodal images of oropharyngeal cancer.- A U-Net convolutional neural network with multiclass Dice loss for automated segmentation of tumors and lymph nodes from head and neck cancer PET/CT images.- Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation.- Swin UNETR for tumor and lymph node delineation of multicentre oropharyngeal cancer patients with PET/CT imaging.- Simplicity is All You Need: Out-of-the-Box nnUNet followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT.- Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer.- Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers.- Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images.- LC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning.- Towards Tumour Graph Learning for Survival Prediction in Head NeckCancer Patients.- Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images.- Head and neck cancer localization with Retina Unet for automated segmentation and time-to-event prognosis from PET/CT images.- HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG-PET/CT images.- Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network.- Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer.- Deep learning and radiomics based PET/CT image feature extractionfrom auto segmented tumor volumes for recurrence-free survival prediction in oropharyngeal cancer patients.
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head and neck cancer;automatic segmentations;classification;computer vision;computerized tomography;deep learning;medical imaging;health informatics;image analysis;image processing;image segmentation;machine learning;medical images;neural networks;pattern recognition;performance, design, evaluation;segmentation methods;radiomics
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT 1.- Automated head and neck tumor segmentation from 3D PET/CTHECKTOR 2022 challenge report.- A Coarse-to-Fine Ensembling Framework for Head and Neck Tumorand Lymph Segmentation in CT and PET Images.- A General Web-based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images.- Octree Boundary Transfiner: Effcient Transformers for Tumor Segmentation Refinement.- Head and Neck Primary Tumor and Lymph Node Auto-Segmentationfor PET/CT Scans.- Fusion-based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques.- Stacking Feature Maps of Multi-Scaled Medical Images in U-Net for 3DHead and Neck Tumor Segmentation.- A fine-tuned 3D U-net for primary tumor and affected lymph nodessegmentationin fused multimodal images of oropharyngeal cancer.- A U-Net convolutional neural network with multiclass Dice loss for automated segmentation of tumors and lymph nodes from head and neck cancer PET/CT images.- Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation.- Swin UNETR for tumor and lymph node delineation of multicentre oropharyngeal cancer patients with PET/CT imaging.- Simplicity is All You Need: Out-of-the-Box nnUNet followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT.- Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer.- Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers.- Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images.- LC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning.- Towards Tumour Graph Learning for Survival Prediction in Head NeckCancer Patients.- Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images.- Head and neck cancer localization with Retina Unet for automated segmentation and time-to-event prognosis from PET/CT images.- HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG-PET/CT images.- Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network.- Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer.- Deep learning and radiomics based PET/CT image feature extractionfrom auto segmented tumor volumes for recurrence-free survival prediction in oropharyngeal cancer patients.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
head and neck cancer;automatic segmentations;classification;computer vision;computerized tomography;deep learning;medical imaging;health informatics;image analysis;image processing;image segmentation;machine learning;medical images;neural networks;pattern recognition;performance, design, evaluation;segmentation methods;radiomics
