31 citations · 43 across the 9 of their papers we have counts for
9 papers
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…
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset
Frauke Wilm, Marco Fragoso, Christof A. Bertram +7
In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated d…
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
Frauke Wilm, Marco Fragoso, Christof A. Bertram +7
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the p…
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks
Mathias Öttl, Jana Mönius, Matthias Rübner +9
Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalenc…
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset
Frauke Wilm, Marco Fragoso, Christian Marzahl +8
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approac…