Abstract
Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are usually dependent on the training images and show poor performance on unseen domains. In this work, we present a multi-stage mitosis detection method based on a Cascade R-CNN developed to be sequentially more selective against false positives. On the preliminary test set, the algorithm scores an F\(_1\) score of 0.7492.
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Razavi, S., Dambandkhameneh, F., Androutsos, D., Done, S., Khademi, A. (2022). Cascade R-CNN for MIDOG Challenge. 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_13
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DOI: https://doi.org/10.1007/978-3-030-97281-3_13
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