activity
20142022
most citedNoise transfer for unsupervised domain adaptation of retinal OCT images

16 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.GN20225 cited

Uncertainty Quantification for Atlas-Level Cell Type Transfer

Jan Engelmann, Leon Hetzel, Giovanni Palla +3

Single-cell reference atlases are large-scale, cell-level maps that capture cellular heterogeneity within an organ using single cell genomics. Given their size and cellular diversi…

cs.LG20226 cited

Sparsity in Continuous-Depth Neural Networks

Hananeh Aliee, Till Richter, Mikhail Solonin +3

Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories. While different typ…

cs.CV202216 cited

Noise transfer for unsupervised domain adaptation of retinal OCT images

Valentin Koch, Olle Holmberg, Hannah Spitzer +4

Optical coherence tomography (OCT) imaging from different camera devices causes challenging domain shifts and can cause a severe drop in accuracy for machine learning models. In th…

cs.CV2016

Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields

Gerda Bortsova, Michael Sterr, Lichao Wang +6

Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understo…

cs.GR2014

MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data

Justin Feigelman, Fabian J. Theis, Carsten Marr

Background: Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of disti…