Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.

中文翻译：

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解码表型筛选：图像表示的比较分析

高内涵筛选 (HCS) 等生物医学成像技术对于药物发现很有价值，但高昂的成本限制了制药公司的使用。为了解决这个问题，JUMP-CP 联盟发布了海量的化学和遗传扰动的开放图像数据集，为深度学习研究提供了宝贵的资源。在这项工作中，我们的目标是利用 JUMP-CP 数据集开发 HCS 数据的通用表示模型，主要是使用 U2OS 细胞和 CellPainting 协议生成的数据，使用监督和自监督学习方法。我们提出了一种评估协议，使用流行的表型筛选数据集评估它们在行动模式和属性预测任务上的表现。结果表明，使用来自多个联盟合作伙伴的数据的自我监督方法提供了对批次效应更稳健的表示，同时实现了与标准方法相当的性能。与其他结论一起，它为 HCS 图像表示模型的训练策略提供了建议。