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Titlebook: Head and Neck Tumor Segmentation and Outcome Prediction; Third Challenge, HEC Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu Conference

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发表于 2025-3-21 16:04:30 | 显示全部楼层 |阅读模式
书目名称Head and Neck Tumor Segmentation and Outcome Prediction
副标题Third Challenge, HEC
编辑Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Head and Neck Tumor Segmentation and Outcome Prediction; Third Challenge, HEC Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu Conference
描述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 presented, as well as an overview paper, were carefully reviewed and selected from 24 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 883 delineated  PET/CT images was made available for training. .
出版日期Conference proceedings 2023
关键词head and neck cancer; automatic segmentations; classification; computer vision; computerized tomography;
版次1
doihttps://doi.org/10.1007/978-3-031-27420-6
isbn_softcover978-3-031-27419-0
isbn_ebook978-3-031-27420-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Hung Chu,Luis Ricardo De la O Arévalo,Wei Tang,Baoqiang Ma,Yan Li,Alessia De Biase,Stefan Both,JohanRe­ sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index i978-1-4757-0410-5978-1-4757-0408-2
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Louis Rebaud,Thibault Escobar,Fahad Khalid,Kibrom Girum,Irène Buvat­ sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 978-1-4757-0422-8978-1-4757-0420-4
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Mingyuan Meng,Lei Bi,Dagan Feng,Jinman KimResearch (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is
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Kai Wang,Yunxiang Li,Michael Dohopolski,Tao Peng,Weiguo Lu,You Zhang,Jing Wang­ sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 978-1-4757-0422-8978-1-4757-0420-4
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,Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement,network feature maps in addition to the raw modalities as input and selects regions of interest from these. These are then processed with a transformer network and decoded with a CNN. We evaluated our framework with Dice Similarity Coefficient (DSC) 0.76426 for the first task of the Head and Neck Tu
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,Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans,l deep learning frameworks, including 3D U-Net, MNet, Swin Transformer, and nnU-Net (both 2D and 3D), to segment CT and PET images of primary tumors (GTVp) and cancerous lymph nodes (GTVn) automatically. Our investigations led us to three promising models for submission. Via 5-fold cross validation
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