Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL’s feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.

中文翻译：

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CLANet：使用明场图像进行跨批次细胞系识别的综合框架

细胞系认证在生物医学领域发挥着至关重要的作用，确保研究人员使用准确识别的细胞。监督深度学习通过细胞成像研究细胞形态特征，在细胞系识别方面取得了显着的进步。然而，生物批次（生物批次）效应是由于数据生成的不同时间而产生的一个重大问题，导致基础数据分布发生重大变化，从而使来自不同批次培养物的细胞系之间的可靠区分变得复杂。为了应对这一挑战，我们推出了 CLANet，这是一种使用明场图像进行跨批次细胞系识别的开创性框架，专门设计用于解决三种不同的生物批次效应。我们提出了一种细胞簇级选择方法来有效捕获细胞密度变化，并提出一种自监督学习策略来管理图像质量变化，从而产生可靠的斑块表示。此外，我们采用多实例学习（MIL）来有效聚合实例级特征以进行细胞系识别。我们创新的时间序列分段采样模块进一步增强了 MIL 的特征学习能力，减少了批次间不同孵化时间带来的偏差。我们使用来自阿斯利康全球细胞库的 93 个实验生物批次的 32 个细胞系的数据来验证 CLANet。我们的结果表明，CLANet 优于相关方法（例如域适应、MIL），证明了其在解决细胞系识别中的生物批次效应方面的有效性。