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Supervised learning of every possible pathology is unrealistic for manyprimary care applications like health screening. Image anomaly detectionmethods that learn normal appearance from only healthy data have shownpromising results recently. We propose an alternative to imagereconstruction-based and image embedding-based methods and propose a newself-supervised method to tackle pathological anomaly detection. Our approachoriginates in the foreign patch interpolation (FPI) strategy that has shownsuperior performance on brain MRI and abdominal CT data. We propose to use abetter patch interpolation strategy, Poisson image interpolation (PII), whichmakes our method suitable for applications in challenging data regimes. PIIoutperforms state-of-the-art methods by a good margin when tested on surrogatetasks like identifying common lung anomalies in chest X-rays or hypo-plasticleft heart syndrome in prenatal, fetal cardiac ultrasound images. Codeavailable at https://github.com/jemtan/PII.

Detecting Outliers with Poisson Image Interpolation