Outlier Detection in Large Radiological Datasets using UMAP
This addresses dataset quality issues for researchers and practitioners in biomedical imaging, though it is incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of detecting outliers in large radiological datasets by applying the UMAP algorithm, which formed independent clusters to identify anomalies such as errors and inconsistencies in datasets like ChestX-ray14, CheXpert, and MURA.
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.