default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDHECKTOR@MICCAI 2022: Singapore
- Vincent Andrearczyk
, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge:
Head and Neck Tumor Segmentation and Outcome Prediction - Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Lecture Notes in Computer Science 13626, Springer 2023, ISBN 978-3-031-27419-0 - Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joël Castelli, Catherine Cheze Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D. Fuller, Mario Jreige, Yomna Khamis, Agustina La Greca Saint-Esteven, Abdallah Sherif Radwan Mohamed, Mohamed A. Naser, John O. Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem A. Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge:
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. 1-30 - Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He, Daguang Xu:
Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report. 31-37 - Xiao Sun, Chengyang An, Lisheng Wang:
A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images. 38-46 - Hao Jiang, Jason Haimerl, Xuejun Gu, Weiguo Lu:
A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images. 47-53 - Anthony Wang, Ti Bai, Dan Nguyen, Steve B. Jiang:
Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement. 54-60 - Arnav Jain, Julia Huang, Yashwanth Ravipati, Gregory Cain, Aidan Boyd, Zezhong Ye, Benjamin H. Kann:
Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans. 61-69 - Seyed Masoud Rezaeijo, Ali Harimi, Mohammad R. Salmanpour:
Fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques. 70-76 - Yaying Shi, Xiaodong Zhang, Yonghong Yan:
Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation. 77-85 - Agustina La Greca Saint-Esteven, Laura Motisi, Panagiotis Balermpas, Stephanie Tanadini-Lang:
A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer. 86-93 - Shadab Ahamed, Luke Polson, Arman Rahmim:
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. 94-106 - Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci:
Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation. 107-113 - Hung Chu, Luis Ricardo De la O. Arévalo, Wei Tang, Baoqiang Ma, Yan Li, Alessia De Biase, Stefan Both, Johannes A. Langendijk, Peter M. A. van Ooijen, Nanna Maria Sijtsema, Lisanne van Dijk:
Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach. 114-120 - Louis Rebaud, Thibault Escobar, Fahad Khalid, Kibrom Girum, Irène Buvat:
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. 121-134 - Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim:
Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer. 135-143 - Kai Wang, Yunxiang Li, Michael Dohopolski, Tao Peng, Weiguo Lu, You Zhang, Jing Wang:
Recurrence-Free Survival Prediction Under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers. 144-153 - Hui Xu, Yihao Li, Wei Zhao, Gwenolé Quellec, Lijun Lu, Mathieu Hatt:
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images. 154-165 - Vajira Thambawita, Andrea M. Storås, Steven Alexander Hicks, Pål Halvorsen, Michael A. Riegler:
MLC at HECKTOR 2022: The Effect and Importance of Training Data When Analyzing Cases of Head and Neck Tumors Using Machine Learning. 166-177 - Ángel Víctor Juanco-Müller, João F. C. Mota, Keith A. Goatman, Corné Hoogendoorn:
Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients. 178-191 - Qing Lyu:
Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images. 192-201 - Yiling Wang, Elia Lombardo, Lili Huang, Claus Belka, Marco Riboldi, Christopher Kurz, Guillaume Landry:
Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images. 202-211 - Zohaib Salahuddin, Yi Chen, Xian Zhong, Nastaran Mohammadian Rad, Henry C. Woodruff, Philippe Lambin:
HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images. 212-220 - Jianan Chen, Anne L. Martel:
Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network. 221-229 - Mohammad R. Salmanpour, Ghasem Hajianfar, Mahdi Hosseinzadeh, Seyed Masoud Rezaeijo, Mohammad Mehdi Hosseini, Ehsanhosein Kalatehjari, Ali Harimi, Arman Rahmim:
Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer. 230-239 - Baoqiang Ma, Yan Li, Hung Chu, Wei Tang, Luis Ricardo De la O. Arévalo, Jiapan Guo, Peter M. A. van Ooijen, Stefan Both, Johannes A. Langendijk, Lisanne van Dijk, Nanna Maria Sijtsema:
Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients. 240-254
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-02-26 20:33 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
