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Publicaties

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

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Reducing and controlling metabolic active tumor volume prior to CAR T-cell infusion can improve survival outcomes in patients with large B-cell lymphoma

Towards the global integration of a comprehensive toxicity risk profile for HNC patients

Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer

Comparison of Machine-Learning and Deep-Learning Methods for the Prediction of Osteoradionecrosis Resulting From Head and Neck Cancer Radiation Therapy

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

DASS Good: Explainable Data Mining of Spatial Cohort Data

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images

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