Data Science / Machine Learning for Safe Medical Diagnostics
We work on robust and interpretable algorithms that enable machine learning algorithms to be applied in the clinical workflow in ophthalmology.
Machine Learning for Safe Medical Diagnostics
We work on machine learning for safe medical diagnostics. We are particularly interested in critical aspects for clinical acceptance of machine learning, such as methods for interpretability, uncertainty quantification, robustness, and generalization to real world settings. Our research is mostly applied in ophthalmic imaging using modalities such as fundus photography and optical coherence tomography. We collaborate closely with the university hospital, and ultimately aim to improve clinical decision making in the context of major causes of vision impairment such as age-related macular degeneration and diabetic retinopathy.
