Detecting Outliers with Foreign Patch Interpolation

Jeremy TanImperial College London, Benjamin HouImperial College London, James BattenImperial College London, Huaqi QiuImperial College London, Bernhard KainzImperial College London
April 2022 issue
Publication date: 2022/04/14
PDF · arXiv · Code


In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.
Our code is available at


self-supervised learning · medical imaging · outlier detection

Bibtex @article{melba:2022:013:tan, title = "Detecting Outliers with Foreign Patch Interpolation", authors = "Tan, Jeremy and Hou, Benjamin and Batten, James and Qiu, Huaqi and Kainz, Bernhard", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "April 2022 issue", year = "2022" }

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