  Automated detection of mitotic figures in histopathology images is a
challenging task: here, we present the different steps that describe the
strategy we applied to participate in the MIDOG 2021 competition. The purpose
of the competition was to evaluate the generalization of solutions to images
acquired with unseen target scanners (hidden for the participants) under the
constraint of using training data from a limited set of four independent source
scanners. Given this goal and constraints, we joined the challenge by proposing
a straight-forward solution based on a combination of state-of-the-art deep
learning methods with the aim of yielding robustness to possible
scanner-related distributional shifts at inference time. Our solution combines
methods that were previously shown to be efficient for mitosis detection: hard
negative mining, extensive data augmentation, rotation-invariant convolutional
networks.
  We trained five models with different splits of the provided dataset. The
subsequent classifiers produced F1-scores with a mean and standard deviation of
0.747+/-0.032 on the test splits. The resulting ensemble constitutes our
candidate algorithm: its automated evaluation on the preliminary test set of
the challenge returned a F1-score of 0.6828.
