Bram van Ginneken
Professor
Bram van Ginneken is Professor of Medical Image Analysis at Radboud University Medical Center and chairs the Diagnostic Image Analysis Group. He also works for Fraunhofer MEVIS in Bremen, Germany, and is a founder of Thirona, a company that develops software and provides services for medical image analysis. He studied Physics at Eindhoven University of Technology and Utrecht University. In 2001, he obtained his PhD at the Image Sciences Institute on Computer-Aided Diagnosis in Chest Radiography. He has (co-)authored over 250 publications in international journals. He is member of the Editorial Board of Medical Image Analysis. He pioneered the concept of challenges in medical image analysis.
- Current research projects:
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- Natural language processing of radiology reports for lesion detection
- AI4Adhesion
- AI4ImageGuidedIntervention
- AI for Radiology
- AI-based Diagnostic Decision Making in Primary Care
- Automated Detection and Grading of Hip Osteoartritis
- BabyChecker: AI for low-cost prenatal ultrasound
- Body composition assessment in 3D CT and MR images
- Deep PCA
- Few-shot learning for medical image segmentation
- Fracture detection in hand radiographs
- Automated Detection of Developmental Hip Dysplasia
- AI-driven incidental lung nodule analysis
- Robust identification of the L3 vertebra
- MRI-based decision support tool for patients with chronic lower back pain
- Artificial intelligence for lung cancer screening
- MERAI Lab
- Building multi-modal interactive health records
- Oncology
- PARADIGM: The diagnostic potential of 3D ultrasound with AI in maternity care
- Automatic quantification of traumatic brain injuries
- Development and validation of a deep-learning system for wisdom tooth removal
- Finished research projects:
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- Automated AAA detection on CT scans
- AMI
- Image Analysis in Acute Stroke
- MistraL: Mitigation Strategies for COVID-19 transmission in Lesotho Using AI on Chest X-rays and Novel Rapid Diagnostic Tests
- Machine Learning in Acute Care: Liver & Spleen
- Detecting and characterizing vertebral fractures in CT scans
- Software:
- Member of: