105 citations · 151 across the 16 of their papers we have counts for
3 papers · 1 filter
Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
Purpose: To analyze and remove protected feature effects in chest radiograph embeddings of deep learning models. Methods: An orthogonalization is utilized to remove the influence o…
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
The application of deep learning in survival analysis (SA) allows utilizing unstructured and high-dimensional data types uncommon in traditional survival methods. This allows to ad…
Survival-oriented embeddings for improving accessibility to complex data structures
Tobias Weber, Michael Ingrisch, Matthias Fabritius +2
Deep learning excels in the analysis of unstructured data and recent advancements allow to extend these techniques to survival analysis. In the context of clinical radiology, this…