5 papers
Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction
Dominik Winter, Dominik Vonficht, Loïc Le Bescond +6
H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution…
MSDM: Generating Task-Specific Pathology Images with a Multimodal Conditioned Diffusion Model for Cell and Nuclei Segmentation
Dominik Winter, Mai Bui, Monica Azqueta Gavaldon +3
Scarcity of annotated data, particularly for rare or atypical morphologies, present significant challenges for cell and nuclei segmentation in computational pathology. While manual…
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…