@inproceedings{efc36703cd32413caaab3839d096cfee,
title = "Towards Robust General Medical Image Segmentation",
abstract = "The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data. Several attacks and defenses have been proposed to improve the performance of Deep Neural Networks under the presence of adversarial noise in the natural image domain. However, robustness in computer-aided diagnosis for volumetric data has only been explored for specific tasks and with limited attacks. We propose a new framework to assess the robustness of general medical image segmentation systems. Our contributions are two-fold: (i) we propose a new benchmark to evaluate robustness in the context of the Medical Segmentation Decathlon (MSD) by extending the recent AutoAttack natural image classification framework to the domain of volumetric data segmentation, and (ii) we present a novel lattice architecture for RObust Generic medical image segmentation (ROG). Our results show that ROG is capable of generalizing across different tasks of the MSD and largely surpasses the state-of-the-art under sophisticated adversarial attacks.",
keywords = "Adversarial attacks, Adversarial training, General medical segmentation, Robustness assessment",
author = "Laura Daza and P{\'e}rez, \{Juan C.\} and Pablo Arbel{\'a}ez",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2021",
doi = "10.1007/978-3-030-87199-4\_1",
language = "English",
isbn = "9783030871987",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--13",
editor = "\{de Bruijne\}, Marleen and Cattin, \{Philippe C.\} and St{\'e}phane Cotin and Nicolas Padoy and Stefanie Speidel and Yefeng Zheng and Caroline Essert",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings",
address = "Germany",
}