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Related Experiment Video

Updated: May 24, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Overcoming Domain Shift in Atypical Mitotic Figure Detection with Deep Ensemble Learning.

Sara Krauss1, Ellena Spiess2, Daniel Hieber2

  • 1IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, University of Augsburg, Germany.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
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This study introduces a robust pipeline for detecting atypical mitotic figures (AMFs) in histopathology. The method shows strong generalization across diverse datasets, offering a reliable tool for clinical analysis.

Area of Science:

  • Computational pathology
  • Histopathology image analysis
  • Machine learning in oncology

Background:

  • Accurate classification of atypical mitotic figures (AMFs) is crucial for cancer prognosis.
  • Current deep learning models struggle with generalization in diverse histopathological settings.
  • Developing robust AMF detection pipelines is essential for clinical translation.

Purpose of the Study:

  • To present a robust and reproducible pipeline for the detection of atypical mitotic figures (AMFs).
  • To evaluate the generalization capabilities of the developed pipeline across varied datasets and clinical conditions.

Main Methods:

  • Compiled a large dataset from three public histopathology image sources.
  • Trained an ensemble of three ConvNeXt models using a 3-fold cross-validation bagging strategy.
Keywords:
Digital PathologyMedical Image ClassificationMitosis

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  • Validated the pipeline on an internal hold-out set and the MICCAI MIDOG2025 Challenge.

    • Main Results:

    • Achieved 89.18% balanced accuracy on an internal hold-out dataset.
    • Demonstrated excellent generalization in the MICCAI MIDOG2025 Challenge with 88.94% balanced accuracy, ranking #8.
    • Minimal performance drop indicates robustness against variations in tissue, staining, and scanners.

    Conclusions:

    • The developed pipeline offers a validated, foundational tool for clinical AMF analysis.
    • The robust generalization performance supports its applicability in diverse real-world histopathology scenarios.
    • This work addresses the critical need for reliable automated AMF detection in cancer diagnostics.