  Mitosis detection is one of the fundamental tasks in computational pathology,
which is extremely challenging due to the heterogeneity of mitotic cell. Most
of the current studies solve the heterogeneity in the technical aspect by
increasing the model complexity. However, lacking consideration of the
biological knowledge and the complex model design may lead to the overfitting
problem while limited the generalizability of the detection model. In this
paper, we systematically study the morphological appearances in different
mitotic phases as well as the ambiguous non-mitotic cells and identify that
balancing the data and feature diversity can achieve better generalizability.
Based on this observation, we propose a novel generalizable framework (MitDet)
for mitosis detection. The data diversity is considered by the proposed
diversity-guided sample balancing (DGSB). And the feature diversity is
preserved by inter- and intra- class feature diversity-preserved module
(InCDP). Stain enhancement (SE) module is introduced to enhance the
domain-relevant diversity of both data and features simultaneously. Extensive
experiments have demonstrated that our proposed model outperforms all the SOTA
approaches in several popular mitosis detection datasets in both internal and
external test sets using minimal annotation efforts with point annotations
only. Comprehensive ablation studies have also proven the effectiveness of the
rethinking of data and feature diversity balancing. By analyzing the results
quantitatively and qualitatively, we believe that our proposed model not only
achieves SOTA performance but also might inspire the future studies in new
perspectives. Source code is at https://github.com/Onehour0108/MitDet.
