MorphoCLIP：用于细胞绘画图像扰动匹配的文本监督对比学习
MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images
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中文总结 AI 辅助
MorphoCLIP是一种仅训练跨通道模块和投影层的对比模型，可实现细胞绘画图像与扰动描述的双向匹配，文本监督有助于组织相关数据，但化合物与遗传扰动的匹配仍待解决。
中文摘要 AI 辅助
细胞绘画显微镜可捕捉细胞在化学或遗传扰动后的变化，将这些图像与产生它们的扰动关联起来，能让大型成像筛选的搜索和解释更便捷，但该任务难度较大，因为生物效应细微且技术变异显著。我们提出MorphoCLIP，这是一种将细胞绘画特征与化合物、CRISPR敲除及ORF过表达的文本描述关联的对比模型。该模型保持视觉和语言主干冻结，仅训练紧凑的跨通道模块和投影层，因此可在单个消费级GPU上训练。在保留的CPJUMP1数据上，MorphoCLIP支持双向搜索：从细胞图像到其扰动描述，以及从描述到匹配的细胞图像。在两种情况下，正确匹配出现在前十结果中的频率远高于随机预期。添加重复样本对齐损失可使重复实验的特征更一致，不过这种改进尚未转化为可靠的基因-化合物匹配。基因感知标签和板校正也未表现出一致的检索增益。这些发现表明，文本监督可帮助组织化学和遗传细胞绘画数据，然而，将化合物与遗传扰动匹配仍是一个未解决的问题。
英文摘要
Cell Painting microscopy captures how cells change after a chemical or genetic perturbation. Connecting these images to the perturbations that produced them could make large imaging screens easier to search and interpret, but the task remains difficult because biological effects are subtle and technical variation is substantial. We introduce MorphoCLIP, a contrastive model that links Cell Painting profiles with text descriptions of compounds, CRISPR knockouts, and ORF overexpressions. The model keeps its vision and language backbones frozen and trains only a compact cross-channel module and projection layers, so it can be trained on a single consumer GPU. On held-out CPJUMP1 data, MorphoCLIP searches in both directions: from a cell image to its perturbation description and from a description to matching cell images. In both cases, a correct match appears among the top ten results much more often than expected by chance. Adding a replicate-alignment loss makes profiles from repeated experiments more consistent, although this improvement does not yet translate into reliable gene-compound matching. Gene-aware labels and plate correction also show no consistent retrieval benefit. These findings suggest that text supervision can help organize chemical and genetic Cell Painting data. Matching compounds with genetic perturbations, however, remains an open problem.
发表机构
- Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。
