Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algorithms are a promising solution to improve tumour prognostication. Pathologists are the gold standard for database development, however, labelling errors may hamper development of accurate algorithms. In the present work we evaluated the benefit of multi-expert consensus (n = 3, 5, 7, 9, 11) on algorithmic performance. While training with individual databases resulted in highly variable F1 scores, performance was notably increased and more consistent when using the consensus of three annotators. Adding more annotators only resulted in minor improvements. We conclude that databases by few pathologists and high label accuracy may be the best compromise between high algorithmic performance and time investment.

https://arxiv.org/abs/2012.02495

Paper:
https://doi.org/10.1007/978-3-658-33198-6_56

Inter-Annotator Variability:
https://doi.org/10.1007/978-3-030-61166-8_22 (Bertram et al.)
https://doi.org/10.1371/journal.pone.0161286 (Veta et al.)
PHH3 Reference Staining:
https://doi.org/10.1109/TMI.2018.2820199 (Tellez et al.)