ArXiv Preprint
Detection of pathologies is a fundamental task in medical imaging and the
evaluation of algorithms that can perform this task automatically is crucial.
However, current object detection metrics for natural images do not reflect the
specific clinical requirements in pathology detection sufficiently. To tackle
this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for
evaluating algorithms for pathology detection in medical images, especially in
chest X-rays. RoDeO evaluates different errors directly and individually, and
reflects clinical needs better than current metrics. Extensive evaluation on
the ChestX-ray8 dataset shows the superiority of our metrics compared to
existing ones. We released the code at https://github.com/FeliMe/RoDeO and
published RoDeO as pip package (rodeometric).
Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert
2023-03-03
