162 citations · 514 across the 55 of their papers we have counts for
11 papers · 1 filter
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
Jonathan Ganz, Jonas Ammeling, Emely Rosbach +4
Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key ide…
Learning-Based Autonomous Navigation, Benchmark Environments and Simulation Framework for Endovascular Interventions
Lennart Karstensen, Harry Robertshaw, Johannes Hatzl +8
Endovascular interventions are a life-saving treatment for many diseases, yet suffer from drawbacks such as radiation exposure and potential scarcity of proficient physicians. Robo…
Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation
Frauke Wilm, Mathias Öttl, Marc Aubreville +1
Recent advances in computer-aided diagnosis for histopathology have been largely driven by the use of deep learning models for automated image analysis. While these networks can pe…
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…
On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images
Jonathan Ganz, Christian Marzahl, Jonas Ammeling +20
The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However…
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
Ahmed Gomaa, Yixing Huang, Amr Hagag +16
Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning mod…
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…
Re-identification from histopathology images
Jonathan Ganz, Jonas Ammeling, Samir Jabari +2
In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primar…
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…
Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides
Chloé Puget, Jonathan Ganz, Julian Ostermaier +7
Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit…