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Cell line authentication plays a crucial role in the biomedical field,ensuring researchers work with accurately identified cells. Supervised deeplearning has made remarkable strides in cell line identification by studyingcell morphological features through cell imaging. However, batch effects, asignificant issue stemming from the different times at which data is generated,lead to substantial shifts in the underlying data distribution, thuscomplicating reliable differentiation between cell lines from distinct batchcultures. To address this challenge, we introduce CLANet, a pioneeringframework for cross-batch cell line identification using brightfield images,specifically designed to tackle three distinct batch effects. We propose a cellcluster-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 multipleinstance learning(MIL) for effective aggregation of instance-level features forcell line identification. Our innovative time-series segment sampling modulefurther enhances MIL's feature-learning capabilities, mitigating biases fromvarying incubation times across batches. We validate CLANet using data from 32cell lines across 93 experimental batches from the AstraZeneca Global CellBank. Our results show that CLANet outperforms related approaches (e.g. domainadaptation, MIL), demonstrating its effectiveness in addressing batch effectsin cell line identification.

CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images