The density of mitotic figures within tumor tissue is known to be highly
correlated with tumor proliferation and thus is an important marker in tumor
grading. Recognition of mitotic figures by pathologists is known to be subject
to a strong inter-rater bias, which limits the prognostic value.
State-of-the-art deep learning methods can support the expert in this
assessment but are known to strongly deteriorate when applied in a different
clinical environment than was used for training. One decisive component in the
underlying domain shift has been identified as the variability caused by using
different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has
been to propose and evaluate methods that counter this domain shift and derive
scanner-agnostic mitosis detection algorithms. The challenge used a training
set of 200 cases, split across four scanning systems. As a test set, an
additional 100 cases split across four scanning systems, including two
previously unseen scanners, were given. The best approaches performed on an
expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95:
0.704-0.781). In this paper, we evaluate and compare the approaches that were
submitted to the challenge and identify methodological factors contributing to
better performance.

本文评估和比较不同算法的性能表现，针对临床用途中的整张切片扫描仪领域差异问题，提出和评估了一种扫描器不可知的有助于病理学家判读分级的异常分裂细胞检测算法。