Abstract: Comprehensive Multi-domain Dataset for Mitotic Figure Detection

The density of mitotic figures is a well-established diagnostic marker for tumor malignancy across many tumor types and species. At the same time, the identification of mitotic figures in hematoxylin and eosin-stained tissue slices is known to have a high inter-rater variability, reducing its reproducibility. Hence, mitotic figure identification in tumor tissue is a task worth automating using deep learning models. Additionally, there is high variability in tissue across labs, tumor types, and scanning devices, which leads to a covariant domain shift responsible for reducing the performance of many models. To provide a data foundation for the investigation of robustness and training of robust mitotic figure recognition models alike, we introduced the MIDOG++ dataset [1]. The dataset builds on the training data sets of the MIDOG 2021 and 2022 MICCAI challenges and extends them by two additional tumor types. In total, the dataset features regions of interest with a size of 2mm2 from 503 histological specimens across seven different tumor types (breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma). The annotation database, created from a consensus of three pathologists, aided by a machine learning algorithm to reduce the risk of missing mitotic figures, contains in total 11,937 mitotic figures. In our paper, we have demonstrated that there is a considerable domain gap between individual domains, but also that a combination of multiple domains yields robust mitotic figure detectors across tumor types and scanners.