Zusammenfassung
Shortage of annotated data is one of the greatest bottlenecks related to biomedical image analysis in general, and surgical data science (SDS) in particular. Meta learning studies howlearning systems can increase in efficiency through experience and could thus evolve as an important concept to overcome data sparsity. A core capability of meta learningbased approaches is the identification of similar previous tasks given a new task. We recently addressed this problem and presented the concept of task fingerprinting [1], which involves representing a task (comprising images and labels), by a vector of fixed length irrespective of data set size, types of labels or specific resolutions (Fig. 1).
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Godau P, Maier-Hein L. Task fingerprinting for meta learning in biomedical image analysis. Med Image Comput Comput Assist Interv. 2021:436–46.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Godau, P., Maier-Hein, L. (2022). Abstract: Task Fingerprinting for Meta Learning in Biomedical Image Analysis. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_55
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DOI: https://doi.org/10.1007/978-3-658-36932-3_55
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