Gradual domain adaptation for segmenting whole slide images showing pathological variability
Gadermayr M, Eschweiler D, Klinkhammer BM, Boor P, Merhof D (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 10884 LNCS
Pages Range: 461-469
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Cherbourg, FRA
ISBN: 9783319942100
DOI: 10.1007/978-3-319-94211-7_50
Abstract
Although there is a strong demand, the utilization of automated segmentation approaches in histopathological imaging is often inhibited by a high degree of variability. To tackle the thereby arising challenges, we propose an unsupervised “gradual” domain adaptation framework which exploits the knowledge that disease progression is a gradual process and that the approximate level-of-progression is known. We extend an existing approach by adding two methods for regularization of the fully-unsupervised adaptation process. Experiments performed on three datasets corresponding to three different renal pathologies showed excellent segmentation accuracies. The framework is not restricted to the considered task, but can also be adapted to other similar (biomedical) application scenarios.
Involved external institutions
Germany (DE)How to cite
APA:
Gadermayr, M., Eschweiler, D., Klinkhammer, B.M., Boor, P., & Merhof, D. (2018). Gradual domain adaptation for segmenting whole slide images showing pathological variability. In Driss Mammass, Fathallah Nouboud, Alamin Mansouri, Abderrahim El Moataz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 461-469). Cherbourg, FRA: Springer Verlag.
MLA:
Gadermayr, Michael, et al. "Gradual domain adaptation for segmenting whole slide images showing pathological variability." Proceedings of the 8th International Conference on Image and Signal Processing, ICISP 2018, Cherbourg, FRA Ed. Driss Mammass, Fathallah Nouboud, Alamin Mansouri, Abderrahim El Moataz, Springer Verlag, 2018. 461-469.
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