School of Computing and Mathematical Sciences

Spotlight on CLANet -- Deep learning in cell line authentication

CLANet is an AI framework developed by Professor H Zhou's and collaborators, and funded by AstraZeneca Ltd.

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.

Visualization of the successful cases from the PC3 cell line. Red bounding boxes highlight the corresponding cell patches selected via the Cell Cluster-Level Selection (CCS) method.

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