2 citations · 3 across the 4 of their papers we have counts for
4 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…
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…
An unobtrusive quality supervision approach for medical image annotation
Sonja Kunzmann, Mathias Öttl, Prathmesh Madhu +2
Image annotation is one essential prior step to enable data-driven algorithms. In medical imaging, having large and reliably annotated data sets is crucial to recognize various dis…
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation
Mathias Öttl, Jana Mönius, Christian Marzahl +8
Supervised deep learning has shown state-of-the-art performance for medical image segmentation across different applications, including histopathology and cancer research; however,…