102 citations · 135 across the 8 of their papers we have counts for
3 papers · 1 filter
Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model
Dominik Winter, Nicolas Triltsch, Marco Rosati +8
Creating in-silico data with generative AI promises a cost-effective alternative to staining, imaging, and annotating whole slide images in computational pathology. Diffusion model…
ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation
Dominik Winter, Nicolas Triltsch, Philipp Plewa +5
The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the po…
Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images
Nicolas Brieu, Nicolas Triltsch, Philipp Wortmann +4
Generative models enable the translation from a source image domain where readily trained models are available to a target domain unseen during training. While Cycle Generative Adv…