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-Reference-Cited by-同舟云学术

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

Published:2023 Issue: Volume: Page:94-106
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Ahamed Shadab^ORCID,Polson Luke,Rahmim Arman^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-27420-6_10

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3. Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images (2022). https://doi.org/10.48550/ARXIV.2201.04138, https://arxiv.org/abs/2201.04138

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5. Ardila, D., Kiraly, A., Bharadwaj, S., et al.: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25(4), 954–961 (2019). https://doi.org/10.1038/s41591-019-0447-x. Accessed 07 Sep 2022

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1. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT;Lecture Notes in Computer Science;2023