用高效微调让大模型精准识别罕见癌细胞分裂形态。
Parameter-efficient fine-tuning (PEFT) of Vision Foundation Models for Atypical Mitotic Figure Classification
- 用低秩适配（LoRA）高效微调视觉大模型，节省参数。
- 最佳模型在测试集上达88.37%平衡准确率，排名前列。
- 适合病理图像分析、小样本医学分类任务的研究者。
非典型有丝分裂（AMF）是与肿瘤侵袭性和不良预后相关的罕见异常细胞分裂，其检测因形态细微、类别不平衡及病理学家间差异大而困难。MIDOG 2025挑战赛设立了专门的非典型有丝分裂分类赛道，推动深度学习方法的系统评估。本研究采用大型视觉基础模型Virchow、Virchow2和UNI，结合低秩适配（LoRA）进行参数高效微调。通过不同LoRA秩值及随机与分组数据划分的广泛实验，评估模型在多种条件下的鲁棒性。最优方案为使用LoRA秩8的Virchow模型，配合三折交叉验证集成，在预测试集上达到88.37%的平衡准确率，位列挑战赛排行榜并列第9。结果表明，结合高效适配策略的基础模型在非典型有丝分裂分类中具有潜力，但仍需提升特异性和跨域泛化能力。
原文摘要 · Abstract (English)
Atypical mitotic figures (AMFs) are rare abnormal cell divisions associated with tumor aggressiveness and poor prognosis. Their detection remains a significant challenge due to subtle morphological cues, class imbalance, and inter-observer variability among pathologists. The MIDOG 2025 challenge introduced a dedicated track for atypical mitosis classification, enabling systematic evaluation of deep learning methods. In this study, we investigated the use of large vision foundation models, including Virchow, Virchow2, and UNI, with Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. We conducted extensive experiments with different LoRA ranks, as well as random and group-based data splits, to analyze robustness under varied conditions. Our best approach, Virchow with LoRA rank 8 and ensemble of three-fold cross-validation, achieved a balanced accuracy of 88.37% on the preliminary test set, ranking joint 9th in the challenge leaderboard. These results highlight the promise of foundation models with efficient adaptation strategies for the classification of atypical mitosis, while underscoring the need for improvements in specificity and domain generalization.
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