Atypical mitotic figures (AMFs) are clinically relevant indicators of abnormal cell division, yet their reliable detection remains challenging due to morphological ambiguity and scanner variability. In this work, we investigated three variants of adapting the pathology foundation model UNI2 for the MIDOG2025 Track 2 challenge: (1) LoRA + UNI2, (2) VPT + UNI2 + Vahadane Normalizer, and (3) VPT + UNI2 + GRL + Stain TTA. We observed that the integration of Visual Prompt Tuning (VPT) with stain normalization techniques contributed to improved generalization. The best robustness was achieved by further incorporating test-time augmentation (TTA) with Vahadane and Macenko stain normalization. Our final submission achieved a balanced accuracy of 0.8837 and an ROC-AUC of 0.9513 on the preliminary leaderboard, ranking within the top 10 teams. These results suggest that prompt-based adaptation combined with stain-normalization TTA offers a promising strategy for atypical mitosis classification under diverse imaging conditions.

翻译：非典型有丝分裂图像（AMFs）是细胞异常分裂的临床相关指标，但由于其形态学模糊性和扫描仪差异性，其可靠检测仍具挑战性。本研究针对MIDOG2025 Track 2挑战赛，探索了三种基于病理学基础模型UNI2的适配变体：（1）LoRA + UNI2，（2）VPT + UNI2 + Vahadane标准化器，以及（3）VPT + UNI2 + GRL + 染色TTA。我们观察到，视觉提示调优（VPT）与染色标准化技术的结合有助于提升模型的泛化能力。通过进一步整合Vahadane与Macenko染色标准化的测试时增强（TTA）策略，获得了最佳的鲁棒性表现。我们的最终提交在初步排行榜上取得了0.8837的平衡准确率与0.9513的ROC-AUC值，位列前十名。这些结果表明，基于提示的模型适配结合染色标准化TTA，为多样化成像条件下的非典型有丝分裂分类提供了一种具有前景的策略。