Head and Neck Tumor Segmentation and Outcome Prediction Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings /
This book constitutes the Third 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, on September 22, 2022. The 22 contributions presente...
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| Format: | Elektronski eKnjiga |
| Jezik: | angleščina |
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Springer Nature Switzerland : Imprint: Springer,
2023.
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| Izdaja: | 1st ed. 2023. |
| Serija: | Lecture Notes in Computer Science,
13626 |
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| Online dostop: | https://doi.org/10.1007/978-3-031-27420-6 |
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- 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/CT HECKTOR 2022 challenge report
- A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and 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-Segmentation for 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 3D Head and Neck Tumor Segmentation
- A fine-tuned 3D U-net for primary tumor and affected lymph nodes segmentationin 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 Neck Cancer 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 extraction from auto segmented tumor volumes for recurrence-free survival prediction in oropharyngeal cancer patients.