7 citations · 17 across the 8 of their papers we have counts for
4 papers · 1 filter
Leveraging image captions for selective whole slide image annotation
Jingna Qiu, Marc Aubreville, Frauke Wilm +4
Acquiring annotations for whole slide images (WSIs)-based deep learning tasks, such as creating tissue segmentation masks or detecting mitotic figures, is a laborious process due t…
Analysing Diffusion Segmentation for Medical Images
Mathias Öttl, Siyuan Mei, Frauke Wilm +8
Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspir…
Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation
Mathias Öttl, Frauke Wilm, Jana Steenpass +9
Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generat…
Rethinking U-net Skip Connections for Biomedical Image Segmentation
Frauke Wilm, Jonas Ammeling, Mathias Öttl +3
The U-net architecture has significantly impacted deep learning-based segmentation of medical images. Through the integration of long-range skip connections, it facilitated the pre…