- Our paper on Federated Electronic Health Records is accepted at Lancet Digital Health
- Our paper on Label Noise-Robust Learning is accepted at TMLR
- Check out the video presentation of our work on bounding data reconstruction attacks presented at this year's Theory and Practice of Differential Privacy workshop
- Our papers on bounding data reconstruction attacks, equivariant deep learning with differential privacy and membership inference against semantic segmentation models were accepted among others at TPDP and AISec.
- New pre-prints: Explainable 2D Vision Models for 3D Medical Data and Extended Graph Assessment Metrics for Graph Neural Networks
- Our paper on uncertainty propagation through deep learning pipelines was accepted as a spotlight at UNSURE@MICCAI 2023.
- Our paper on unsupervised anomaly detection was accepted to Transactions on Medical Imaging.
- My co-authors and I were awarded the publication prize of the Bavarian Centre for Cancer Research (BZKF) for our work on deep learning-assisted immunological image analysis.
- Our paper utilising Bayesian deep learning for analysing high-resolution microscopy images in immunology is now out in Cancer Cell. Check it out here.
- New pre-print on privacy-utility trade-offs and membership inference attacks against graph neural networks for medical applications with collaborators from TUM. Check it out here.
- New pre-print on bounding data reconstruction in differentially private deep learning with collaborators from DeepMind and TUM. Check it out here.