SULBA: A Task-Agnostic Data Augmentation Framework for Deep Learning in Medical Image Analysis

Data augmentation is a foundational component of modern deep learning for enhancing robustness and generalization. However, medical imaging lacks a universally reliable augmentation strategy, forcing researchers into an inefficient “augmentation lottery” that hinders experimental progress and reproducibility. We introduce Stepwise Upper and Lower Boundaries Augmentation (SULBA), a simple, parameter-free framework designed to eliminate per-task augmentation tuning. SULBA generates training variations through stepwise cyclic shifts applied along data dimensions, making it inherently applicable to 2D, 3D, and higher-dimensional medical imaging data. Benchmarking across 27 publicly available datasets spanning classification and segmentation tasks, and 10 convolutional and transformer-based architectures, demonstrates that SULBA achieves the highest overall performance rank and consistently outperforms 16 widely used standard augmentation techniques. By delivering robust and reliable improvements without task- or parameter-specific tuning, SULBA establishes a principled universal default for data augmentation in medical imaging, with the potential to accelerate the development of generalizable and reproducible medical AI systems.