Laurin Lux

It is shown that predictions on a patient's unseen future data can change significantly if a model observed that same patient's anonymised historical data during training, a phenomenon known as memorisation bias, which exists across diverse data modalities and model architectures, and over prolonged time spans.

Jointly modeling aleatoric and epistemic uncertainties can dramatically enhance prediction reliability in high-dimensional outputs, outperforming traditional methods.

Overconfident predictions in medical imaging segmentation can be corrected with a simple gradient adjustment that enhances model calibration without sacrificing accuracy.