Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification

arXiv:2508.21041v4 Announce Type: replace-cross Abstract: Atypical mitotic figures (AMFs) indicate abnormal cell division associated with poor prognosis. Their detection remains difficult due to low prevalence, subtle morphology, and inter-observer variability. The MItosis DOmain Generalization (MIDOG) 2025 challenge introduces a benchmark for AMF classification across multiple domains. In this work, we fine-tuned the recently published DINOv3-H+ vision transformer, pretrained on natural images, using low-rank adaptation (LoRA), training only 1.3M parameters. We combine this with extensive augmentation and a domain-weighted Focal Loss to better handle the strong domain heterogeneity in the dataset. Despite the large shift between natural images and histopathology, our fine-tuned DINOv3 transfers effectively, reaching first place on the final test set. These results highlight the advantages of DINOv3 pretraining and underline the efficiency and robustness of our fine-tuning strategy, yielding state-of-the-art results for the atypical mitosis classification challenge in MIDOG 2025. Our code is publicly available on GitHub.

Source: arXiv cs.CV | 2026-08-11