  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, 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 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 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 batch effects
in cell line identification.
