Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge

Author:

Andrearczyk Vincent,Oreiller Valentin,Boughdad Sarah,Le Rest Catherine Cheze,Tankyevych Olena,Elhalawani Hesham,Jreige Mario,Prior John O.,Vallières Martin,Visvikis Dimitris,Hatt Mathieu,Depeursinge Adrien

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference88 articles.

1. Abdallah, N., Xu, H., Marion, J.-M., Tauber, C., Carlier, T., Chauvet, P., Lu, L., Hatt, M., 2022. Predicting progression-free survival from FDG PET/CT images in head and neck cancer : comparison of different pipelines and harmonization strategies in the HECKTOR 2021 challenge dataset. In: Proceedings of the IEEE NSS-MIC.

2. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach;Aerts;Nat. Commun.,2014

3. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M., 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In: Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

4. A coarse-to-fine framework for head and neck tumor segmentation in CT and PET images;An,2022

5. Multi-task deep segmentation and radiomics for automatic prognosis in head and neck cancer;Andrearczyk,2021

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