Zusammenfassung
The annotation of gigapixel-sized whole slide images (WSI) in digital pathology can be time-intensive, especially when generating annotations for training deep segmentation models. Instead of requesting annotations for the fullWSI, region-based active learning (AL) allows to specify selected regions for annotation in an iterative process, reducing annotation while maintaining segmentation performance. Existing methods for section selection onWSI evaluate the informativeness of a quadratic grid of a predefined size of \(l \times l\) pixels according to a suitable informativeness criterion and then select the \(k\) most informative regions. Our experiments showthat the benefit of this method strongly depends on the choice of these two hyperparameters, i.e., the AL step size, and that a suboptimal AL step size can result in uninformative or redundant annotation requests [1].We evaluate our approach on the publicly available CAMELYON16 dataset and show that it consistently achieves higher sampling efficiency measured by annotated area compared to the reference approach across various AL step sizes.With only 2.6% of tissue area annotated, we achieve the same performance compared to a full annotation setting and thereby substantially reduce the costs of annotating a WSI dataset for the task of segmentation. Our approach can in theory be applied with any informativeness measure, with future work looking closer into an improved characterization of annotation effort. The source code is available at https://github.com/DeepMicroscopy/AdaptiveRegionSelection.
Chapter PDF
Similar content being viewed by others
References
Qiu J, Wilm F, Öttl M, Schlereth M, Liu C, Heimann T et al. Adaptive region selection for active learning in whole slide image semantic segmentation. Proc MICCAI. Springer Nature Switzerland, 2023:90–100.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Qiu, J. et al. (2024). Abstract: Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-658-44037-4_6
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-44036-7
Online ISBN: 978-3-658-44037-4
eBook Packages: Computer Science and Engineering (German Language)