用自监督预训练模型解决小样本显微图像蛋白定位难题
Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
- 用ImageNet-1k和HPA FOV预训练的DINO-ViT模型直接迁移至OpenCell数据集
- 零样本下最高F1达0.822，微调后提升至0.860，单细胞级表现最优
- 适合缺乏标注数据的生物图像分析任务，尤其关注蛋白定位研究者
背景：特定任务的显微图像数据集通常规模较小，难以训练出鲁棒特征的深度学习模型。尽管自监督学习（SSL）通过在大规模领域相关数据集上预训练已展现出潜力，但其在不同染色协议与通道配置下的泛化能力仍待探索。本文评估了在ImageNet-1k和HPA FOV上预训练的SSL模型在OpenCell数据集上的表现，包括无微调、两种通道不匹配策略及不同微调数据比例的情形，并分析了标注子集上的单细胞嵌入。结果：基于DINO的ViT骨干网络在未微调时即能良好迁移至OpenCell；HPA FOV预训练模型实现最高零样本性能（宏F1 0.822 ± 0.007），微调后进一步提升至0.860 ± 0.013。在单细胞层面，该模型在所有邻域大小下均取得最佳近邻性能（宏F1 ≥ 0.796）。结论：如DINO等自监督方法在大规模相关数据集上预训练后，可有效提取适用于小规模任务数据集的深度特征，支持后续微调。
原文摘要 · Abstract (English)
Background: Task-specific microscopy datasets are often small, making it difficult to train deep learning models that learn robust features. While self-supervised learning (SSL) has shown promise through pretraining on large, domain-specific datasets, generalizability across datasets with differing staining protocols and channel configurations remains underexplored. We investigated the generalizability of SSL models pretrained on ImageNet-1k and HPA FOV, evaluating their embeddings on OpenCell with and without fine-tuning, two channel-mismatch strategies, and varying fine-tuning data fractions. We additionally analyzed single-cell embeddings on a labeled OpenCell subset. Result: DINO-based ViT backbones pretrained on HPA FOV or ImageNet-1k transfer well to OpenCell even without fine-tuning. The HPA FOV-pretrained model achieved the highest zero-shot performance (macro $F_1$ 0.822 $\pm$ 0.007). Fine-tuning further improved performance to 0.860 $\pm$ 0.013. At the single-cell level, the HPA single-cell-pretrained model achieved the highest k-nearest neighbor performance across all neighborhood sizes (macro $F_1$ $\geq$ 0.796). Conclusion: SSL methods like DINO, pretrained on large domain-relevant datasets, enable effective use of deep learning features for fine-tuning on small, task-specific microscopy datasets.
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