Towards Robust General Medical Image Segmentation


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Adversarial Robustness in Neural Networks

Neural networks have demonstrated outstanding performance in recognition tasks. However, these methods are highly vulnerable to imperceptible corruptions in the input images.

Ground truth
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Prediction on a clean image
gif pred
Prediction on adversarial example
gif pred-adv

RObust General Segmentation (ROG)

We introduce a lattice architecture for general medical image segmentation


Adversarial Robusntess Benchmark

We introduce a new benchmark to reliably assess adversarial robustness on the Medical Segmentation Decathlon. Our benchmarck includes 5 state-of-the-art adversarial attacks that we expanded to the domain of 3D segmentation:

  • PGD
  • APGD-DLR (only for non-binary tasks)
  • FAB
  • Square Attack
Benchmark graph


You can get access to our full paper here. If you find our work useful, please use the following BibTeX entry for citation:

  title={Towards Robust General Medical Image Segmentation},
  author={Daza, Laura and P{\'e}rez, Juan C and Arbel{\'a}ez, Pablo},


Photo Laura Laura Daza
photo Juan C Juan C. Pérez
Photo Pablo Pablo Arbeláez