Zusammenfassung
Deep-learning-based pipelines have shown the potential to revolutionalize microscopy image diagnostics by providing visual augmentations and evaluations to a pathologist. However, to match human performance, the methods rely on the availability of vast amounts of high-quality labeled data, which poses a significant challenge. To circumvent this, augmented labeling methods, also known as expert-algorithm-collaboration, have recently become popular.
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Marzahl C, Bertram CA, Aubreville M, et al. Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides. Proc MICCAI. 2020; p. 24–32.
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Marzahl, C. et al. (2021). Abstract: Are Fast Labeling Methods Reliable?. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_71
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DOI: https://doi.org/10.1007/978-3-658-33198-6_71
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