3 papers
cs.CV2026
Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance
Vanessa Emanuela Guarino, Claudia Winklmayr, Jannik Franzen +7
Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous dri…
cs.CV2025
SelfAdapt: Unsupervised Domain Adaptation of Cell Segmentation Models
Fabian H. Reith, Jannik Franzen, Dinesh R. Palli +2
Deep neural networks have become the go-to method for biomedical instance segmentation. Generalist models like Cellpose demonstrate state-of-the-art performance across diverse cell…
cs.CV2025
PhenoBench: A Comprehensive Benchmark for Cell Phenotyping
Claudia Winklmayr, Jerome Luescher, Nora Koreuber +6
Digital pathology has seen the advent of a wealth of foundational models (FM), yet to date their performance on cell phenotyping has not been benchmarked in a unified manner. We th…