  The success of machine learning algorithms heavily relies on the quality of
samples and the accuracy of their corresponding labels. However, building and
maintaining large, high-quality datasets is an enormous task. This is
especially true for biomedical data and for meta-sets that are compiled from
smaller ones, as variations in image quality, labeling, reports, and archiving
can lead to errors, inconsistencies, and repeated samples. Here, we show that
the uniform manifold approximation and projection (UMAP) algorithm can find
these anomalies essentially by forming independent clusters that are distinct
from the main (good) data but similar to other points with the same error type.
As a representative example, we apply UMAP to discover outliers in the publicly
available ChestX-ray14, CheXpert, and MURA datasets. While the results are
archival and retrospective and focus on radiological images, the graph-based
methods work for any data type and will prove equally beneficial for curation
at the time of dataset creation.
