MitoDet: Simple and Robust Mitosis Detection

Dexl J, Benz M, Bruns V, Kuritcyn P, Wittenberg T (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13166 LNCS

Pages Range: 53-57

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Strasbourg FR

ISBN: 9783030972806

DOI: 10.1007/978-3-030-97281-3_7

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.

Authors with CRIS profile

Thomas Wittenberg Chair of Visual Computing

Involved external institutions

Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie (IISB) DE Germany (DE)

How to cite

APA:

Dexl, J., Benz, M., Bruns, V., Kuritcyn, P., & Wittenberg, T. (2022). MitoDet: Simple and Robust Mitosis Detection. In Marc Aubreville, David Zimmerer, Mattias Heinrich (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 53-57). Strasbourg, FR: Springer Science and Business Media Deutschland GmbH.

MLA:

Dexl, Jakob, et al. "MitoDet: Simple and Robust Mitosis Detection." Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, Strasbourg Ed. Marc Aubreville, David Zimmerer, Mattias Heinrich, Springer Science and Business Media Deutschland GmbH, 2022. 53-57.

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