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2,328
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Head and Neck Tumor Segmentation : First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions
other
Author(s):
Kanchan Ghimire
,
Quan Chen
,
Xue Feng
Publication date
(Online):
January 13 2021
Publisher:
Springer International Publishing
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Value-based Healthcare
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
,
Philipp Fischer
,
Thomas Brox
(2015)
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Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
Vincent Andrearczyk
,
Valentin Oreiller
,
Mario Jreige
…
(2021)
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Author and book information
Book Chapter
Publication date (Print):
2021
Publication date (Online):
January 13 2021
Pages
: 78-84
DOI:
10.1007/978-3-030-67194-5_9
SO-VID:
e11c6453-f409-4af1-af68-2ab22c342fd7
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Book chapters
pp. 1
Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
pp. 22
Two-Stage Approach for Segmenting Gross Tumor Volume in Head and Neck Cancer with CT and PET Imaging
pp. 28
The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel ‘Squeeze & Excitation’ Blocks
pp. 37
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
pp. 44
Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network
pp. 53
Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT Images
pp. 59
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET Images
pp. 78
Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions
pp. 85
Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images
pp. 99
GAN-Based Bi-Modal Segmentation Using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images
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