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Meissen, F.* ; Müller, P.* ; Kaissis, G. ; Rueckert, D.*

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

In: (6th International Conference on Medical Imaging with Deep Learning, MIDL 2023, 10-12 July 2023, Nashville). 2023. 568-585 (Proceedings of Machine Learning Research ; 227)
Verlagsversion
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).
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Metric ; Object Detection ; Pathology Detection
Konferenztitel 6th International Conference on Medical Imaging with Deep Learning, MIDL 2023
Konferzenzdatum 10-12 July 2023
Konferenzort Nashville
Quellenangaben Band: 227, Heft: , Seiten: 568-585 Artikelnummer: , Supplement: ,
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)