Abstract
Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called domain shift. An important source of a domain shift is introduced by different microscopes and their camera systems, which noticeably change the colour representation of digitized images. In this method description, we present our submitted algorithm for the Mitosis Domain Generalization Challenge [1], which employs a RetinaNet [5] trained with strong data augmentation and achieves an F1 score of 0.7138 on the preliminary test set.
This work was supported by the Bavarian Ministry of Economic Affairs, Regional Develop- ment and Energy through the Center for Analytics - Data - Applications (ADA-Center) within “BAYERN DIGITAL II” and by the BMBF (16FMD01K, 16FMD02 and 16FMD03).
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Dexl, J., Benz, M., Bruns, V., Kuritcyn, P., Wittenberg, T. (2022). MitoDet: Simple and Robust Mitosis Detection. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_7
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