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Computer Science > Computer Vision and Pattern Recognition

arXiv:2303.01920 (cs)
[Submitted on 3 Mar 2023]

Title:Robust Detection Outcome: A Metric for Pathology Detection in Medical Images

Authors:Felix Meissen, Philip Müller, Georgios Kaissis, Daniel Rueckert
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Abstract: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 this https URL and published RoDeO as pip package (rodeometric).
Comments: Accepted at MIDL 2023
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2303.01920 [cs.CV]
  (or arXiv:2303.01920v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2303.01920

Submission history

From: Philip Müller [view email]
[v1] Fri, 3 Mar 2023 13:45:13 UTC (30,137 KB)
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