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Detecting Outliers with Poisson Image Interpolation

Tan J, Hou B, Day TG, Simpson JM, Rueckert D, Kainz B (2021)

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Publication Language: English

Publication Status: Published

Publication Type: Authored book, Volume of book series

Publication year: 2021

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 581-591

ISBN: 9783030872397

DOI: 10.1007/978-3-030-87240-3_56

Open Access Link: https://arxiv.org/abs/2107.02622

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.

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How to cite

APA:

Tan, J., Hou, B., Day, T.G., Simpson, J.M., Rueckert, D., & Kainz, B. (2021). Detecting Outliers with Poisson Image Interpolation. Springer Science and Business Media Deutschland GmbH.

MLA:

Tan, Jeremy, et al. Detecting Outliers with Poisson Image Interpolation. Springer Science and Business Media Deutschland GmbH, 2021.

BibTeX: Download