用提示调优+染色归一化提升癌细胞分裂异常检测准确率
Adaptive Learning Strategies for Mitotic Figure Classification in MIDOG2025 Challenge
- 用视觉提示调优适配基础病理模型，提升对不同扫描仪的适应性
- 结合染色归一化与测试时增强，平衡准确率达88.37%，ROC-AUC达95.13%
- 适合关注病理图像分析、跨设备泛化能力的研究者
非典型有丝分裂图像是临床中异常细胞分裂的重要指标，但由于形态模糊和扫描仪差异，其可靠检测仍具挑战。本文研究了三种基于病理基础模型UNI2的自适应策略：(1) LoRA + UNI2，(2) VPT + UNI2 + Vahadane归一化，(3) VPT + UNI2 + GRL + 染色TTA。实验发现，视觉提示调优（VPT）结合染色归一化显著提升了模型泛化能力。进一步引入测试时增强（TTA）并融合Vahadane与Macenko染色归一化方法后，取得最佳鲁棒性。最终提交方案在预赛排行榜上实现平衡准确率0.8837和ROC-AUC 0.9513，位列前10名。结果表明，基于提示的适配方法结合染色归一化测试时增强，是应对多样成像条件下的非典型有丝分裂分类的有效策略。
原文摘要 · Abstract (English)
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.
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