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Robust Pan-Cancer Mitotic Figure Detection with YOLOv12

Created by
  • Haebom

Author

Rapha el Bourgade, Guillaume Balezo, Thomas Walter

Outline

This paper presents a deep learning-based approach for detecting mitotic figures, a key prognostic indicator in tumor pathology. Based on the state-of-the-art YOLOv12 object detection architecture, we developed an algorithm for the Mitosis DOmain Generalization (MIDOG) 2025 challenge. We achieved an F1-score of 0.7216 across complex and heterogeneous entire slide regions and ranked second on the final leaderboard.

Takeaways, Limitations

Takeaways:
Development of a powerful mitotic phase detection algorithm using the YOLOv12 architecture.
It achieves high performance even in complex environments, demonstrating its applicability to real-world pathological image analysis.
Get competitive results without using external data.
Limitations:
F1-score 0.7216 shows room for improvement.
There are no specific algorithm details or additional explanations of Limitations in this paper.
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