RT Journal Article
SR Electronic
T1 Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.14.512286
DO 10.1101/2022.10.14.512286
A1 Lin, Alexander
A1 Lu, Alex X.
YR 2022
UL http://biorxiv.org/content/early/2022/10/18/2022.10.14.512286.abstract
AB Data collected by high-throughput microscopy experiments are affected by batch effects, stemming from slight technical differences between experimental batches. Batch effects significantly impede machine learning efforts, as models learn spurious technical variation that do not generalize. We introduce batch effects normalization (BEN), a simple method for correcting batch effects that can be applied to any neural network with batch normalization (BN) layers. BEN aligns the concept of a “batch” in biological experiments with that of a “batch” in deep learning. During each training step, data points forming the deep learning batch are always sampled from the same experimental batch. This small tweak turns the batch normalization layers into an estimate of the shared batch effects between images, allowing for these technical effects to be standardized out during training and inference. We demonstrate that BEN results in dramatic performance boosts in both supervised and unsupervised learning, leading to state-of-the-art performance on the RxRx1-Wilds benchmark.1Competing Interest StatementThe authors have declared no competing interest.