1 citations · 3 across the 12 of their papers we have counts for
15 papers · 1 filter
Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation
Tristan Kirscher, Markus Bujotzek, Yannick Kirchhoff +5
Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV),…
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming a…
VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation
Maximilian Rokuss, Moritz Langenberg, Yannick Kirchhoff +8
We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences,…
Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models
Maximilian Rokuss, Yannick Kirchhoff, Fabian Isensee +1
Whole-body PET/CT is a cornerstone of oncological imaging, yet accurate lesion segmentation remains challenging due to tracer heterogeneity, physiological uptake, and multi-center…
Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
Constantin Ulrich, Tassilo Wald, Fabian Isensee +1
The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) presents a significant challenge in neuroimaging due to the diverse characteristics of these lesion…
nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
Alexandra Ertl, Stefan Denner, Robin Peretzke +8
Landmark detection is central to many medical applications, such as identifying critical structures for treatment planning or defining control points for biometric measurements. Ho…