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Publications

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression

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

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics

Deformable MRI Sequence Registration for AI-Based Prostate Cancer Diagnosis

Does FDG-PET/CT for incidentally found pulmonary lesions lead to a cascade of more incidental findings?

Evaluating Biparametric Versus Multiparametric Magnetic Resonance Imaging for Diagnosing Clinically Significant Prostate Cancer: An International, Paired, Noninferiority, Confirmatory Observer Study.

Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy

Is radiology's future without medical images?

Is work overload associated with diagnostic errors on 18F-FDG-PET/CT?

Press/media

Lek in de pijpleiding

Pilot 'AI in radiology training' resounding success

Consortium ROBUST receives NWO funding for research into reliable AI

1,9 miljoen euro subsidie voor terugdringen van scantijd MRI bij prostaatkanker

Subsidie voor onderzoek naar snellere MRI-scan met AI

1,9 miljoen euro subsidie voor snellere MRI-scan met artificial intelligence

Subsidie voor onderzoek naar het verkorten van de duur van MRI-scans

Bijna 2 miljoen euro subsidie voor snellere MRI-scan met artificial intelligence

RUG-onderzoeker wil MRI-scan bij prostaatkanker terugbrengen naar kwartier