  Recognition of mitotic figures in histologic tumor specimens is highly
relevant to patient outcome assessment. This task is challenging for algorithms
and human experts alike, with deterioration of algorithmic performance under
shifts in image representations. Considerable covariate shifts occur when
assessment is performed on different tumor types, images are acquired using
different digitization devices, or specimens are produced in different
laboratories. This observation motivated the inception of the 2022 challenge on
MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated
histologic tumor images from six different domains and evaluated the
algorithmic approaches for mitotic figure detection provided by nine challenge
participants on ten independent domains. Ground truth for mitotic figure
detection was established in two ways: a three-expert consensus and an
independent, immunohistochemistry-assisted set of labels. This work represents
an overview of the challenge tasks, the algorithmic strategies employed by the
participants, and potential factors contributing to their success. With an
$F_1$ score of 0.764 for the top-performing team, we summarize that domain
generalization across various tumor domains is possible with today's deep
learning-based recognition pipelines. However, we also found that domain
characteristics not present in the training set (feline as new species, spindle
cell shape as new morphology and a new scanner) led to small but significant
decreases in performance. When assessed against the
immunohistochemistry-assisted reference standard, all methods resulted in
reduced recall scores, but with only minor changes in the order of participants
in the ranking.
