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Abstract: Adversarial Examples as Benchmark for Medical Imaging Neural Networks
Conference paper
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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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Literatur
- 1.Paschali M, Conjeti S, Navarro F, et al. Generalizability vs. robustness: investigating medical imaging networks using adversarial examples. Proc MICCAI. 2018; p. 493–501.Google Scholar
- 2.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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© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019