标题： DINOv3在自然图像上的高效微调用于非典型分裂象分类（MIDOG 2025任务2优胜者） 显示英文标题

标题： Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification (MIDOG 2025 Task 2 Winner)

摘要： 非典型的有丝分裂图像（AMFs）代表与不良预后相关的异常细胞分裂。 然而，由于其发生率低、形态细微以及观察者之间的差异，其检测仍然困难。 MIDOG 2025挑战引入了一个跨多个领域的AMF分类基准。 在这项工作中，我们使用低秩适应（LoRA）对最近发布的DINOv3-H+视觉变压器进行了微调，该模型在自然图像上进行了预训练，仅训练约1.3M参数，并结合了广泛的增强和领域加权焦点损失，以处理领域异质性。 尽管存在领域差距，我们微调后的DINOv3能够有效地转移到组织病理学，最终测试集上取得了第一名。 这些结果突显了DINOv3预训练的优势，并强调了我们微调策略的高效性和鲁棒性，为MIDOG 2025中的非典型有丝分裂分类挑战提供了最先进的结果。 显示英文摘要

摘要： 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.