Robust Detection Outcome: A Metric for Pathology Detection in Medical Images
This work addresses the need for more clinically relevant evaluation metrics in medical imaging, specifically for pathology detection in chest X-rays, but it is incremental as it builds on existing object detection frameworks.
The authors tackled the problem of evaluating pathology detection algorithms in medical images by proposing Robust Detection Outcome (RoDeO), a novel metric that better reflects clinical requirements, and demonstrated its superiority on the ChestX-ray8 dataset.
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).