  The detection of mitotic figures from different scanners/sites remains an
important topic of research, owing to its potential in assisting clinicians
with tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims
to test the robustness of detection models on unseen data from multiple
scanners for this task. We present a short summary of the approach employed by
the TIA Centre team to address this challenge. Our approach is based on a
hybrid detection model, where mitotic candidates are segmented on stain
normalised images, before being refined by a deep learning classifier.
Cross-validation on the training images achieved the F1-score of 0.786 and
0.765 on the preliminary test set, demonstrating the generalizability of our
model to unseen data from new scanners.
