CS-ARM-BN Meta-Learning Method and Comparison
Figure 3: Graphic representation of our method, CS-ARM-BN, and comparison to ARM-BN (Zhang et al.,, 2021 ) . Both are meta-learning methods that are be modified at test-time by using the BN statistics from the target domain (lilac). CS-ARM-BN uses control samples both at training and at inference time, which provides stability when (b) the number of perturbed samples is small or (c) the label distribution is shifted.
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Paper title: Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples Abstract: The central problem in biomedical imaging are batch effects: systematic technical variations unrelated to the biological signal of interest. These batch effects critically undermine experimental reproducibility and are the primary cause of failure of deep learning systems on new experimental batches, preventing their practical use in the real world. Despite years of research, no method has succeeded in closing this performance gap for deep learning models. We propose Control-Stabilized Adaptive Risk Minimization via Batch Normalization (CS-ARM-BN), a meta-learning adaptation method that exploits negative control samples. Such unperturbed reference images are present in every experimental batch by design and serve as stable context for adaptation. We validate our novel method on Mechanism-of-Action (MoA) classification, a crucial task for drug discovery, on the large-scale JUMP-CP dataset. The accuracy of standard ResNets drops from 0.939 $\pm$ 0.005, on the training domain, to 0.862 $\pm$ 0.060 on data from new experimental batches. Foundation models, even after Typical Variation Normalization, fail to close this gap. We are the first to show that meta-learning approaches close the domain gap by achieving 0.935 $\pm$ 0.018. If the new experimental batches exhibit strong domain shifts, such as being generated in a different lab, meta-learning approaches can be stabilized with control samples, which are always available in biomedical experiments. Our work shows that batch effec Passages referencing this figure: s exhibit strong domain shifts, such as being generated in a different lab, meta-learning approaches can be stabilized with control samples, which are always available in biomedical experiments. Our work shows that batch effects in bioimaging data can be effectively neutralized through principled in-context adaptation, which also makes them practically usable and efficient. Machine Learning, ICML Figure 1: Performance of MoA classifier on JUMP-CP data. Error bars represent variance across five cross-validation folds. Green bar: within the training domain, the performance of the classifier is high. Orange bars: The performance of the classifier on images from new experimental batches ("new domain"). Even foundation models with normalization (FM+TVN) suffer performance declines, and domain a
