QC Image Outlier Detection

This tool automatically detects outlier images within medical imaging datasets by identifying samples that deviate from the expected data distribution. It flags out‑of‑distribution (OOD) images that may arise from variations in acquisition protocols, scanner settings, anatomical views, or image quality. By modeling the overall distribution of a dataset, the tool highlights atypical or inconsistent images for manual review, supporting quality control processes and helping ensure the integrity of datasets used for downstream analyses and model training.