Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks in recent years. However, their vulnerability to adversarial attacks remains largely unexplored, raising serious concerns regarding the real-world deployment of tools employing such models in the healthcare sector. This underscores the importance of investigating the robustness of existing models. In this context, our work aims to empirically examine the adversarial robustness across current volumetric segmentation architectures, encompassing Convolutional, Transformer, and Mamba-based models. We extend this investigation across four volumetric segmentation datasets, evaluating robustness under both white box and black box adversarial attacks. Overall, we observe that while both pixel and frequency-based attacks perform reasonably well under white box setting, the latter performs significantly better under transfer-based black box attacks. Across our experiments, we observe transformer-based models show higher robustness than convolution-based models with Mamba-based models being the most vulnerable. Additionally, we show that large-scale training of volumetric segmentation models improves the model's robustness against adversarial attacks. The code and pretrained models will be made available at https://github.com/HashmatShadab/Robustness-of-Volumetric-Medical-Segmentation-Models.

通过对现有的卷积、变压器和曼巴模型进行实证研究，我们观察到基于变压器的模型表现出较高的鲁棒性，而基于卷积和曼巴的模型则表现出较低的鲁棒性。此外，我们还发现大规模训练对于提升体积分割模型的鲁棒性具有积极作用。

评估体积医学分割模型的对抗鲁棒性