Zusammenfassung
Deep learning has been widely adopted as the solution of choice for a plethora of medical imaging applications, due to its state-of-the-art performance and fast deployment. Traditionally, the performance of a deep learning model is evaluated on a test dataset, originating from the same distribution as the training set. This evaluation method provides insight regarding the generalization ability of a model.
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Paschali M, Conjeti S, Navarro F, et al. Generalizability vs. robustness: investigating medical imaging networks using adversarial examples. Proc MICCAI. 2018; p. 493–501.
Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks. Int Conf Learn Representations. 2014;Available from: http://arxiv.org/abs/1312.6199.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Paschali, M., Conjeti, S., Navarro, F., Navab, N. (2019). Abstract: Adversarial Examples as Benchmark for Medical Imaging Neural Networks. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_4
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DOI: https://doi.org/10.1007/978-3-658-25326-4_4
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