8 citations · 10 across the 6 of their papers we have counts for
6 papers
Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology
Matthias Perkonigg, Patrick Rockenschaub, Georg Göbel +1
Accurate brain tumor classification is critical for intra-operative decision making in neuro-oncological surgery. However, existing approaches are restricted to a fixed set of pred…
Robust prediction under missingness shifts
Patrick Rockenschaub, Zhicong Xian, Alireza Zamanian +4
Prediction becomes more challenging with missing covariates. What method is chosen to handle missingness can greatly affect how models perform. In many real-world problems, the bes…
DomainLab: A modular Python package for domain generalization in deep learning
Xudong Sun, Carla Feistner, Alexej Gossmann +10
Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniq…
Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
Robin van de Water, Hendrik Schmidt, Paul Elbers +3
Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance…
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Patrick Rockenschaub, Adam Hilbert, Tabea Kossen +3
Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually w…
Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes
Oliver Carr, Avelino Javer, Patrick Rockenschaub +2
The increase in availability of longitudinal electronic health record (EHR) data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of…