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This paper presents an effective and general data augmentation framework formedical image segmentation. We adopt a computationally efficient anddata-efficient gradient-based meta-learning scheme to explicitly align thedistribution of training and validation data which is used as a proxy forunseen test data. We improve the current data augmentation strategies with twocore designs. First, we learn class-specific training-time data augmentation(TRA) effectively increasing the heterogeneity within the training subsets andtackling the class imbalance common in segmentation. Second, we jointlyoptimize TRA and test-time data augmentation (TEA), which are closely connectedas both aim to align the training and test data distribution but were so farconsidered separately in previous works. We demonstrate the effectiveness ofour method on four medical image segmentation tasks across different scenarioswith two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensiveexperimentation shows that the proposed data augmentation framework cansignificantly and consistently improve the segmentation performance whencompared to existing solutions. Code is publicly available.

Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation