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Adversarial batch representation augmentation for batch correction in high-content cellular screening
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DOI：10.1016/j.knosys.2026.115829.png)
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Abstract
En 中文
• Proposes an Adversarial Batch Representation Augmentation for batch correction. • Models uncertainty of biological batch effects in representation learning. • Uses adversarial learning to identify challenges in the objective function. • Presents a synergistic optimization process for stable training. • Comprehensive experiments validate the effectiveness of the proposed method.
Keywords:
Batch effect
Domain generalization
Cell painting images
siRNA perturbation classification
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