Deep Learning-based mitosis detection in breast cancer histologic samples
arXiv:2109.00816v13 citations
Originality Synthesis-oriented
AI Analysis
This work addresses automated cell counting for cancer diagnosis, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled mitosis detection in breast cancer histology using a Faster RCNN with DenseNet backbone, achieving an F1-score of 0.6645 on the MIDOG 2021 challenge.
This is the submission for mitosis detection in the context of the MIDOG 2021 challenge. It is based on the two-stage objection model Faster RCNN as well as DenseNet as a backbone for the neural network architecture. It achieves a F1-score of 0.6645 on the Preliminary Test Phase Leaderboard.