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Classifying the AMi-Br Mitotic Figure Dataset with AUCMEDI.

Daniel Hieber1,2,3, Friederike Lische-Starnecker1, Johannes Schobel2

  • 1Department of Neuropathology, Pathology, Medical Faculty, University of Augsburg.

Studies in Health Technology and Informatics
|September 3, 2025
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This study explores differentiating atypical (AMF) and normal mitotic figures (NMF) using deep learning. AUCMEDI achieved 85.90% AUC, showing promise for automated mitotic figure analysis in breast cancer research.

Keywords:
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Area of Science:

  • Computational pathology
  • Digital pathology
  • Machine learning in oncology

Background:

  • Mitotic figure (MF) density is a key tumor biomarker.
  • Differentiating atypical MFs (AMF) from normal MFs (NMF) is an emerging research area.
  • AMF density may serve as an independent biomarker, necessitating automated differentiation methods.

Purpose of the Study:

  • To evaluate the AUCMEDI deep learning framework for classifying mitotic figure subtypes.
  • To assess the complexity of differentiating between normal and atypical mitotic figures in breast cancer.
  • To establish a baseline for automated mitotic figure analysis.

Main Methods:

  • Application of the AUCMEDI deep learning framework to the AMi-Br dataset.
  • Utilizing a ConvNeXt-based ensemble for an eight-class subtype classification model.
  • Employing a patient-level cross-validation strategy for training and evaluation.

Main Results:

  • High specificity (≥ 90%) achieved across all mitotic figure classes.
  • Variable sensitivity (0-82%) across subclasses, indicating task complexity.
  • Mean Area Under the Curve (AUC) of 85.90%, surpassing the binary classification baseline (69.8%).

Conclusions:

  • Deep learning shows potential for subclass-level mitotic figure analysis.
  • The study provides insights into the challenges of automated AMF/NMF differentiation.
  • Further model refinement is needed for improved sensitivity and broader clinical application.