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 RCNN developed to be sequentially more selective against false positives. On the preliminary test set, the algorithm scores an F1-score of 0.7492.
@article{arxiv.2109.01085,
title = {Cascade RCNN for MIDOG Challenge},
author = {Salar Razavi and Fariba Dambandkhameneh and Dimitri Androutsos and Susan Done and April Khademi},
journal= {arXiv preprint arXiv:2109.01085},
year = {2021}
}
Comments
Two-page preprint abstract submission for MIDOG challenge, see https://imi.thi.de/midog/, three figures