Arnab, A., Miksik, O., Torr, P.H.: On the robustness of semantic segmentation models to adversarial attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 888–897 (2018)
Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. CoRR. arXiv:abs/1608.04644 (2016)
Carlini, N., Wagner, D.A.: Adversarial examples are not easily detected: bypassing ten detection methods. CoRR. arXiv:abs/1705.07263 (2017)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015)
Finlayson, S.G., Kohane, I.S., Beam, A.L.: Adversarial attacks against medical deep learning systems. arXiv preprint. arXiv:1804.05296 (2018)
Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). CoRR. abs:1605.01397 (2016)
Heimann, T., Meinzer, H.P.: Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. CoRR. arXiv:abs/1607.02533 (2016)
Pena-Betancor, C., et al.: Estimation of the relative amount of hemoglobin in the cup and neuroretinal rim using stereoscopic color fundus images. Invest. Ophthalmol. Vis. Sci. 56(3), 1562–1568 (2015)
Szegedy, C., et al.: Intriguing properties of neural networks. CoRR. arXiv:abs/1312.6199 (2013)
Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1369–1378 (2017)