Detecting Outliers with Poisson Image Interpolation

• URL: http://arxiv.org/abs/2107.02622v1
• Date: Tue, 6 Jul 2021 13:53:17 GMT
• Title: Detecting Outliers with Poisson Image Interpolation
• Authors: Jeremy Tan, Benjamin Hou, Thomas Day, John Simpson, Daniel Rueckert, Bernhard Kainz
• Abstract summary: We propose an alternative to image reconstruction-based and image embedding-based methods to tackle pathological anomaly detection. Our approach originates in the foreign patch pathology (FPI) strategy that has shown superior performance on brain MRI and abdominal CT data. We propose to use a better patch strategy, Poisson image (PII) which makes our method suitable for applications in challenging data regimes.
• Score: 9.928058261360578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearance from only healthy data have shown promising results recently. We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection. Our approach originates in the foreign patch interpolation (FPI) strategy that has shown superior performance on brain MRI and abdominal CT data. We propose to use a better patch interpolation strategy, Poisson image interpolation (PII), which makes our method suitable for applications in challenging data regimes. PII outperforms state-of-the-art methods by a good margin when tested on surrogate tasks like identifying common lung anomalies in chest X-rays or hypo-plastic left heart syndrome in prenatal, fetal cardiac ultrasound images. Code available at https://github.com/jemtan/PII.

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