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Publications

A comparative study of federated learning methods for COVID-19 detection

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist

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

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT

Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules

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

PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer

Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test

Reproducibility of radiomics quality score: an intra- and inter-rater reliability study

Uncertainty-Aware Deep Learning for Segmentation of Primary Tumour and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Centre Cohort

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Press/media

Terugblik Winterschool: ‘Een rijbewijs voor kunstmatige intelligentie en gezondheid’

Kunstmatige intelligentie helpt UMCG bij opsporen ziektes

Hoe kan kunstmatige intelligentie helpen om longkanker eerder op te sporen?

Computer zegt vlek. Zo helpt kunstmatige intelligentie in het UMCG bij het opsporen van ziektes

Hoe kan kunstmatige intelligentie helpen om longkanker eerder op te sporen?

Data Science Center in Health (DASH)

DAME: Deep learning Algorithms for Medical image Evaluation

DAME project

Data control with AI: who’s in charge?

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