Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)

TL;DR

This paper introduces a publicly available dataset of normal and atypical mitotic figures in breast cancer histology, enabling research on their prognostic significance and pattern recognition methods.

Contribution

It provides the first dataset of its kind with expert-labeled mitotic figures, facilitating further research on atypical MFs in breast cancer.

Findings

Balanced accuracy of 0.806 with patch-level split

Balanced accuracy of 0.713 with patient-level split

Dataset includes 3,720 labeled MFs from 223 cases

Abstract

Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4%) and 2,888 normal MFs (77.6%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance. We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split.