Domain Shift as a Confounding Variable in Unsupervised Pathology Detection
Felix Meissen, Ioannis Lagogiannis, Georgios Kaissis, Daniel Rueckert
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.
Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)