14 citations · 33 across the 36 of their papers we have counts for
6 papers · 1 filter
Memorisation bias in medical AI
Moritz A. Knolle, Martin J. Menten, Laurin Lux +5
Medical AI models hold immense potential to improve patient outcomes, but they are also known to unintentionally memorise individual records from their training datasets. While suc…
Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images
Linus Kreitner, Laurin Lux, Carmen Baumann +2
Recent advances in angiographic imaging have enabled longitudinal visualization of the microvasculature. Image processing pipelines based on vessel graphs are able to resolve subtl…
It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
Leonhard F. Feiner, Manuel Nickel, Martin Menten +6
Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-dimensional output spaces. Thi…
When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
Andrea Posada, Wenke Karbole, Bach Ngoc Doan +9
Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the imaged subject are altered, whil…
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Alexander Weers, Daniel Rueckert, Martin J. Menten
Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data. This work evaluates the use of a weighted los…
Step-resolved data attribution for looped transformers
Georgios Kaissis, David Mildenberger, Juan Felipe Gomez +2
We study how individual training examples shape the internal computation of looped transformers, where a shared block is applied for recurrent iterations to enable latent reaso…