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

Abstract

Background/Objectives: 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. To address this challenge, we introduce Stepwise Upper and Lower Boundaries Augmentation (SULBA), a simple, parameter-free framework designed to eliminate per-task augmentation tuning. Methods: 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. To evaluate the efficacy of SULBA as a default DA strategy, we performed benchmarking across 27 publicly available datasets spanning classification and segmentation tasks and 10 convolutional and transformer-based architectures using standard deep learning performance metrics. Results: The results demonstrate that SULBA achieves the highest overall performance and consistently outperforms 16 widely used standard augmentation techniques while delivering robust and reliable improvements without task- or parameter-specific tuning Conclusions: 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.