Zusammenfassung
Deep convolutional neural networks can evidently achieve astonishing accuracies for multiple medical image analysis tasks, in particular segmentation and detection. However, the actual translation of deep learning into clinical practice is so far very limited, in part because their extensive computations rely on specialised GPU hardware that is not easily available in clinical environments.
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Heinrich, M.P., Oktay, O. (2018). Abstract: Exploring Sparsity in CNNs for Medical Image Segmentation BRIEFnet. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_25
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DOI: https://doi.org/10.1007/978-3-662-56537-7_25
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