Domain Shift as a Confounding Variable in Unsupervised Pathology Detection

Felix Meissen, Ioannis Lagogiannis, Georgios Kaissis, Daniel Rueckert

Keywords: Unsupervised Pathology Detection, Domain Shift, Confounders

TL;DR: We show that the good image-level performance of unsupervised pathology detection methods on the Hyper-Kvasir dataset mainly relates to the domain shift between images with and without polyps and not to the actual presence of polyps

Abstract:

Unsupervised Pathology Detection (UPD) has recently received considerable attention in medical image diagnosis.
However, the lack of publicly available benchmark datasets for UPD makes researchers fall back on datasets that were originally created for other tasks.
These datasets may exhibit domain shift that acts as a confounding variable, fooling observers into believing that the models excel at detecting pathologies, while a significant part of the model’s performance is detecting the domain shift.
In this short paper, we show on the example of the Hyper-Kvasir dataset, how confounding variables can dramatically skew the actual performance of pathology detection methods.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Learning with Noisy Labels and Limited Data

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