Robust Pan-Cancer Mitotic Figure Detection with YOLOv12

Detecting mitotic figures (MFs) in histopathology images remains a challenging task. Their quantification traditionally relies on the manual identification of "hot spots" by pathologists, followed by visual counting--an approach that is inherently subjective and may not reliably reflect the true prolifer-ative activity of a tumor. With the rise of digital pathology and artificial intelligence, numerous efforts have been made to automate mitosis detection in order to enhance accuracy, reproducibility, and scalability. Among these, the MItosis DOmain Generalization (MIDOG) challenges have emerged as a key benchmark for evaluating the generalizability of detection algorithms under realistic domain shifts. The 2021 edition (1) addressed scanner-induced variability using breast cancer WSIs, while the 2022 edition (2) extended the scope to include multiple tissue types and species, introducing further biological diversity. The 2025 MIDOG challenge (3) builds on these foundations with the most comprehensive mitosis-annotated dataset to date, and introduces two tasks: (1) detecting mitotic figures in arbitrary tumor tissue, and (2) determining whether a mitotic figure is atypical or normal. These tasks represent a significant step toward developing robust mitosis detection systems that generalize across diverse and complex histological conditions. In this work, we present a high-performance detection pipeline based on the YOLOv12 object detection architecture.