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.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings|
|Editors||Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||11|
|Publication status||Published - 2021|
|Event||24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online|
Duration: 27 Sep 2021 → 1 Oct 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021|
|Period||27/09/21 → 1/10/21|
Bibliographical noteFunding Information:
Acknowledgements. Support from Wellcome Trust IEH Award iFind project  and UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare. JT was supported by the ICL President’s Scholarship.
© 2021, Springer Nature Switzerland AG.
- Outlier detection
- Self-supervised learning