Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification in MIDOG 2025 (2508.21041v1)

Abstract: Atypical mitotic figures (AMFs) are markers of abnormal cell division associated with poor prognosis, yet their detection remains difficult due to low prevalence, subtle morphology, and inter-observer variability. The MIDOG 2025 challenge introduces a benchmark for AMF classification across multiple domains. In this work, we evaluate the recently published DINOv3-H+ vision transformer, pretrained on natural images, which we fine-tuned using low-rank adaptation (LoRA, 650k trainable parameters) and extensive augmentation. Despite the domain gap, DINOv3 transfers effectively to histopathology, achieving a balanced accuracy of 0.8871 on the preliminary test set. These results highlight the robustness of DINOv3 pretraining and show that, when combined with parameter-efficient fine-tuning, it provides a strong baseline for atypical mitosis classification in MIDOG 2025.