Unified Retrieval for Streamlining Biomedical Image Dataset Aggregation and Standardization

Abstract

Advancements in computational power and algorithmic refinements have significantly amplified the impact and applicability of machine learning (ML), particularly in medical imaging. While ML in general thrives on extensive datasets to develop accurate, robust, and unbiased models, medical imaging faces unique challenges, including a scarcity of samples and a predominance of poorly annotated, heterogeneous datasets. This heterogeneity manifests in varied acquisition conditions, target populations, data formats and structures. Data acquisition of large datasets is often additionally hampered by compatibility issues of source specific downloading tools with high-performance computing (HPC) environments. To address these challenges, we introduce the unified retrieval tool (URT), a tool that unifies the acquisition and standardization of diverse medical imaging datasets to the brain imaging data structure (BIDS). Currently, downloads from the cancer imaging archive (TCIA), OpenNeuro and Synapse are supported, easing access to large-scale medical data. URT’s modularity allows the straightforward extension to other sources. Moreover, URT’s compatibility with Docker and Singularity enables reproducible research and easy application on HPCs.