Meta-learning with control samples can close the domain gap caused by batch effects in biomedical imaging, enabling deep learning models to work reliably across different experimental batches and labs without retraining from scratch.
Batch effects—systematic technical variations in biomedical imaging—cause deep learning models to fail on new experimental data. This paper introduces CS-ARM-BN, a meta-learning method that uses negative control samples (unperturbed reference images always present in experiments) to adapt models to new batches.