Towards Robust General Medical Image Segmentation
- URL: http://arxiv.org/abs/2107.04263v1
- Date: Fri, 9 Jul 2021 07:17:05 GMT
- Title: Towards Robust General Medical Image Segmentation
- Authors: Laura Daza, Juan C. P\'erez, Pablo Arbel\'aez
- Abstract summary: We propose a new framework to assess the robustness of general medical image segmentation systems.
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.
- Score: 2.127049691404299
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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