31 citations · 43 across the 9 of their papers we have counts for
5 papers · 1 filter
Decomposition Sampling for Efficient Region Annotations in Active Learning
Jingna Qiu, Frauke Wilm, Mathias Öttl +5
Active learning improves annotation efficiency by selecting the most informative samples for annotation and model training. While most prior work has focused on selecting informati…
Effortless Vision-Language Model Specialization in Histopathology without Annotation
Jingna Qiu, Nishanth Jain, Jonas Ammeling +2
Recent advances in Vision-Language Models (VLMs) in histopathology, such as CONCH and QuiltNet, have demonstrated impressive zero-shot classification capabilities across various ta…
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…
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…
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation
Jingna Qiu, Frauke Wilm, Mathias Öttl +5
The process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. R…