Adversarial Batch Representation Augmentation for Batch Correction in High-Content Cellular Screening
This addresses batch correction for deep learning models in cellular imaging, though it appears incremental as it builds on domain generalization methods.
The paper tackled the problem of batch effects in high-content cellular screening images, which degrade model generalization, by proposing Adversarial Batch Representation Augmentation (ABRA) as a domain generalization solution, achieving state-of-the-art results on siRNA perturbation classification benchmarks like RxRx1 and RxRx1-WILDS.
High-Content Screening routinely generates massive volumes of cell painting images for phenotypic profiling. However, technical variations across experimental executions inevitably induce biological batch (bio-batch) effects. These cause covariate shifts and degrade the generalization of deep learning models on unseen data. Existing batch correction methods typically rely on additional prior knowledge (e.g., treatment or cell culture information) or struggle to generalize to unseen bio-batches. In this work, we frame bio-batch mitigation as a Domain Generalization (DG) problem and propose Adversarial Batch Representation Augmentation (ABRA). ABRA explicitly models batch-wise statistical fluctuations by parameterizing feature statistics as structured uncertainties. Through a min-max optimization framework, it actively synthesizes worst-case bio-batch perturbations in the representation space, guided by a strict angular geometric margin to preserve fine-grained class discriminability. To prevent representation collapse during this adversarial exploration, we introduce a synergistic distribution alignment objective. Extensive evaluations on the large-scale RxRx1 and RxRx1-WILDS benchmarks demonstrate that ABRA establishes a new state-of-the-art for siRNA perturbation classification.