Assessing Inter-Annotator Agreement for Medical Image Segmentation

Artificial Intelligence (AI)-based medical computer vision algorithm training and evaluations depend on annotations and labeling. However, variability between expert annotators introduces noise in training data that can adversely impact the performance of AI algorithms. This study aims to assess, il...

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Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser:	Yang, Feng, Zamzmi, Ghada, Angara, Sandeep, Rajaraman, Sivaramakrishnan, Aquilina, Andre, Xue, Zhiyun, Jaeger, Stefan, Papagiannakis, Emmanouil, Antani, Sameer K
Format:	Artikel
Sprache:	eng
Schlagworte:	Abnormalities agreement Agreements Algorithms Annotations Artificial intelligence Biomedical imaging Cohen’s kappa Computer vision Fleiss’ kappa Heating systems heatmap Image segmentation inter-annotator Kappa coefficient Lesions Medical imaging Quantitative analysis Reliability Reliability analysis STAPLE Training
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