12 citations · 48 across the 21 of their papers we have counts for
7 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…
When Two Wrongs Don't Make a Right" -- Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology
Emely Rosbach, Jonas Ammeling, Sebastian Krügel +24
Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboratio…
Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology
Emely Rosbach, Jonathan Ganz, Jonas Ammeling +2
Artificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI coll…
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…
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis
Marc Aubreville, Jonathan Ganz, Jonas Ammeling +2
The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and com…
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…