Classifying the AMi-Br Mitotic Figure Dataset with AUCMEDI.

Abstract

Introduction: Mitotic figure (MF) density has been established as a key biomarker for certain tumors. Recently, the differentiation between atypical MFs (AMF) and normal MFs (NMFs) has gained increased interest in research, as AMFs density could be an independent biomarker. This results in the challenge of finding an automated, deterministic way to differentiate between AMFs and NMFs.

Methods: In this study, the AUCMEDI deep learning framework is applied to the recently published AMi-Br dataset to get a first bearing on the complexity of the task at hand. The dataset includes eight mitotic subclasses derived from breast cancer samples, four for NMFs and four for AMF. Using a patient-level cross- validation strategy and a ConvNeXt-based ensemble, we trained and evaluated an eight-class subtype classification model.

Results: Our results show high specificity across all classes (≥ 90%), but sensitivity varies significantly between mitotic subclasses (0-82%), reflecting the dataset's inherent challenges. The mean AUC of 85.90% outperforms the binary classification baseline (69.8%).

Conclusion: The results highlight the promise of progress in subclass-level mitotic analysis while pointing to areas for further model refinement.