Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a small amount of annotation is needed for generalisation to novel classes. This is especially seen in medical domains where dense pixel-level annotations are expensive to obtain. In this paper, we propose Regularized Prototypical Neural Ordinary Differential Equation (R-PNODE), a method that leverages intrinsic properties of Neural-ODEs, assisted and enhanced by additional cluster and consistency losses to perform Few-Shot Segmentation (FSS) of organs. R-PNODE constrains support and query features from the same classes to lie closer in the representation space thereby improving the performance over the existing Convolutional Neural Network (CNN) based FSS methods. We further demonstrate that while many existing Deep CNN based methods tend to be extremely vulnerable to adversarial attacks, R-PNODE exhibits increased adversarial robustness for a wide array of these attacks. We experiment with three publicly available multi-organ segmentation datasets in both in-domain and cross-domain FSS settings to demonstrate the efficacy of our method. In addition, we perform experiments with seven commonly used adversarial attacks in various settings to demonstrate R-PNODE's robustness. R-PNODE outperforms the baselines for FSS by significant margins and also shows superior performance for a wide array of attacks varying in intensity and design.

翻译：尽管深度学习模型在图像语义分割方面取得了巨大进展，但它们通常需要大量标注样本，而越来越多关注转向小样本学习（Few-Shot Learning, FSL）等问题设定——仅需少量标注即可泛化至新类别。这在医学领域尤为突出，因为密集的像素级标注成本高昂。本文提出正则化原型神经常微分方程（Regularized Prototypical Neural Ordinary Differential Equation, R-PNODE），该方法利用神经ODE的内在特性，通过额外簇损失和一致性损失进行辅助与增强，实现器官的小样本分割（Few-Shot Segmentation, FSS）。R-PNODE使来自同一类别的支持特征和查询特征在表示空间中更接近，从而提升现有基于卷积神经网络（CNN）的FSS方法的性能。我们还进一步证明，虽然许多现有深度CNN方法极易受到对抗攻击，但R-PNODE对多种此类攻击均表现出更强的对抗鲁棒性。我们在三个公开多器官分割数据集上，于域内和跨域FSS设定下进行实验，验证了方法的有效性。此外，我们还在多种设定下使用七种常见对抗攻击进行实验，以展示R-PNODE的鲁棒性。R-PNODE在FSS任务上显著优于基线方法，并在强度与设计各异的广泛攻击中展现出优越性能。