Cascade RCNN for MIDOG Challenge
This work addresses the challenge of accurate and labor-intensive mitotic counting in breast cancer diagnosis, though it appears incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of automated mitotic cell counting for breast cancer prognosis by developing a multi-stage mitosis detection method based on Cascade RCNN, which achieved an F1-score of 0.7492 on a preliminary test set.
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.