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Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.

, , , , , , , and . Comput. Methods Programs Biomed., (2024)

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Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach., , , , , , , , , and 1 other author(s). HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 114-120. Springer, (2022)Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer., , , , , , , and . Comput. Methods Programs Biomed., (2024)Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test., , , , , , , , and . Comput. Biol. Medicine, (2024)Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images., , , , , , and . HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 109-120. Springer, (2021)Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer., , , , , , , and . HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 308-317. Springer, (2021)Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images., , , , and . CoRR, (2022)