Atypical mitotic figures (AMFs) represent 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 fine-tuned the recently published DINOv3-H+ vision transformer, pretrained on natural images, using low-rank adaptation (LoRA), training only ~1.3M parameters in combination with extensive augmentation and a domain-weighted Focal Loss to handle domain heterogeneity. Despite the domain gap, our fine-tuned DINOv3 transfers effectively to histopathology, 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.

翻译：非典型有丝分裂像（AMFs）是与不良预后相关的异常细胞分裂现象。然而，由于其低发生率、形态学特征细微以及观察者间差异性，其检测仍具挑战性。MIDOG 2025挑战赛提出了一个跨多领域的AMF分类基准。本研究采用低秩自适应（LoRA）方法，对近期发布的基于自然图像预训练的DINOv3-H+视觉Transformer进行微调，仅训练约130万个参数，并结合了大规模数据增强及处理领域异质性的域加权Focal Loss。尽管存在领域差异，我们微调后的DINOv3能有效迁移至组织病理学图像，在最终测试集上获得第一名。这些结果凸显了DINOv3预训练的优势，并证明了我们微调策略的高效性与鲁棒性，从而在MIDOG 2025非典型有丝分裂分类挑战中取得了最先进的性能。