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:尽管在图像语义分解方面深层学习模型取得了巨大进步,但它们通常需要大量附加说明的例子,而且人们越来越关注问题环境,如少点热学习(FSL),因为一般化到新类只需要少量批注。这在医学领域尤为明显,因为那里密集的像素级说明非常昂贵。在本文中,我们建议采用正规化的原型神经普通分等法(R-PNODE),这种方法利用神经值的内在特性,通过更多的集束和一致性损失来实施器官的少点热分解(FSS),从而得到越来越多的关注。R-PNODE限制和查询功能,以便更接近代表空间,从而改善基于FSS的现有革命神经网络(CNN)的批注方法的性能。我们进一步表明,虽然许多基于深点CNN的方法往往极易受到对抗性攻击,但R-PNODE展示了广泛的对抗性强性强度。我们用三种公开的多层次分级分解(FSS)的精度设计(FS-PRS)的精度设计(FS)的精度,同时也展示了我们用于常规性攻击的多级分级分级分级分解性数据,并展示了各种方法。