Computer Science > Computer Vision and Pattern Recognition
[Submitted on 2 Sep 2021 (v1), last revised 26 Sep 2021 (this version, v2)]
Title:Cascade RCNN for MIDOG Challenge
View PDFAbstract: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.
Submission history
From: Salar Razavi [view email][v1] Thu, 2 Sep 2021 17:02:50 UTC (3,697 KB)
[v2] Sun, 26 Sep 2021 21:58:42 UTC (6,916 KB)
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