用现成模型高效还原细胞图像，助力药物发现
Compressing Biology: Evaluating the Stable Diffusion VAE for Phenotypic Drug Discovery
- 用Stable Diffusion的VAE重建细胞图像，保留表型信号
- 重建误差极小，跨多种药物和细胞类型表现稳定
- 适合想快速上手的生物医学研究者
高通量表型筛选生成海量显微图像数据，对生成模型的维度处理能力提出挑战。尽管通用自然图像模型在显微图像分析中日益流行，但其适用性尚未得到定量验证。本文首次系统评估Stable Diffusion变分自编码器（SD-VAE）在重建Cell Painting图像中的表现，涵盖包含多种分子扰动和细胞类型的大型数据集。结果表明，SD-VAE重建能以极小损失保留表型信号，支持其用于显微成像工作流。为衡量重建质量，我们对比了像素级、嵌入层、潜空间及检索任务等多类指标。结果显示，通用特征提取器如InceptionV3在检索任务中表现不逊于甚至优于专用模型，简化未来流程设计。研究为显微图像生成模型评估提供实用指南，并支持在表型药物发现中直接采用现成模型。
原文摘要 · Abstract (English)
High-throughput phenotypic screens generate vast microscopy image datasets that push the limits of generative models due to their large dimensionality. Despite the growing popularity of general-purpose models trained on natural images for microscopy data analysis, their suitability in this domain has not been quantitatively demonstrated. We present the first systematic evaluation of Stable Diffusion's variational autoencoder (SD-VAE) for reconstructing Cell Painting images, assessing performance across a large dataset with diverse molecular perturbations and cell types. We find that SD-VAE reconstructions preserve phenotypic signals with minimal loss, supporting its use in microscopy workflows. To benchmark reconstruction quality, we compare pixel-level, embedding-based, latent-space, and retrieval-based metrics for a biologically informed evaluation. We show that general-purpose feature extractors like InceptionV3 match or surpass publicly available bespoke models in retrieval tasks, simplifying future pipelines. Our findings offer practical guidelines for evaluating generative models on microscopy data and support the use of off-the-shelf models in phenotypic drug discovery.
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