4 citations · 10 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
Lisa Mais, Peter Hirsch, Claire Managan +6
Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morph…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
How Shift Equivariance Impacts Metric Learning for Instance Segmentation
Josef Lorenz Rumberger, Xiaoyan Yu, Peter Hirsch +6
Metric learning has received conflicting assessments concerning its suitability for solving instance segmentation tasks. It has been dismissed as theoretically flawed due to the sh…
Probabilistic Deep Learning for Instance Segmentation
Josef Lorenz Rumberger, Lisa Mais, Dagmar Kainmueller
Probabilistic convolutional neural networks, which predict distributions of predictions instead of point estimates, led to recent advances in many areas of computer vision, from im…