OpenAlex · Aktualisierung stündlich · Letzte Aktualisierung: 10.04.2026, 17:43

Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.

Challenge Results Are Not Reproducible

2023·0 Zitationen·arXiv (Cornell University)Open Access
Volltext beim Verlag öffnen

0

Zitationen

3

Autoren

2023

Jahr

Abstract

While clinical trials are the state-of-the-art methods to assess the effect of new medication in a comparative manner, benchmarking in the field of medical image analysis is performed by so-called challenges. Recently, comprehensive analysis of multiple biomedical image analysis challenges revealed large discrepancies between the impact of challenges and quality control of the design and reporting standard. This work aims to follow up on these results and attempts to address the specific question of the reproducibility of the participants methods. In an effort to determine whether alternative interpretations of the method description may change the challenge ranking, we reproduced the algorithms submitted to the 2019 Robust Medical Image Segmentation Challenge (ROBUST-MIS). The leaderboard differed substantially between the original challenge and reimplementation, indicating that challenge rankings may not be sufficiently reproducible.

Ähnliche Arbeiten

Autoren

Themen

Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationMachine Learning in Materials Science
Volltext beim Verlag öffnen