Capsule Neural Networks utilize capsules, which bind neurons into a single
vector and learn position equivariant features, which makes them more robust
than original Convolutional Neural Networks. CapsNets employ an affine
transformation matrix and dynamic routing with coupling coefficients to learn
robustly. In this paper, we investigate the effectiveness of CapsNets in
analyzing highly sensitive and noisy time series sensor data. To demonstrate
CapsNets robustness, we compare their performance with original CNNs on
electrocardiogram data, a medical time series sensor data with complex patterns
and noise. Our study provides empirical evidence that CapsNets function as
noise stabilizers, as investigated by manual and adversarial attack experiments
using the fast gradient sign method and three manual attacks, including offset
shifting, gradual drift, and temporal lagging. In summary, CapsNets outperform
CNNs in both manual and adversarial attacked data. Our findings suggest that
CapsNets can be effectively applied to various sensor systems to improve their
resilience to noise attacks. These results have significant implications for
designing and implementing robust machine learning models in real world
applications. Additionally, this study contributes to the effectiveness of
CapsNet models in handling noisy data and highlights their potential for
addressing the challenges of noise data in time series analysis.

CapsNets 在分析高敏感、噪音时间序列传感器数据方面具有良好的鲁棒性，通过与原始 CNNs 在心电图数据上的性能比较，证明了其噪音稳定化的功能和優越性。该研究结果对设计和实施鲁棒的机器学习模型在实际应用中具有重要意义，并强调了 CapsNet 模型在处理噪音数据方面的潜力和对时序分析中的噪音数据挑战的应对能力。

胶囊神经网络作为时间序列数据的噪音稳定器

Capsule Neural Networks as Noise Stabilizer for Time Series Data

Capsule Neural Networks (CapsNets) is a novel architecture that utilizes
vector-wise representations formed by multiple neurons. Specifically, the
Dynamic Routing CapsNets (DR-CapsNets) employ an affine matrix and dynamic
routing mechanism to train capsules and acquire translation-equivariance
properties, enhancing its robustness compared to traditional Convolutional
Neural Networks (CNNs). Echocardiograms, which capture moving images of the
heart, present unique challenges for traditional image classification methods.
In this paper, we explore the potential of DR-CapsNets and propose CardioCaps,
a novel attention-based DR-CapsNet architecture for class-imbalanced
echocardiogram classification. CardioCaps comprises two key components: a
weighted margin loss incorporating a regression auxiliary loss and an attention
mechanism. First, the weighted margin loss prioritizes positive cases,
supplemented by an auxiliary loss function based on the Ejection Fraction (EF)
regression task, a crucial measure of cardiac function. This approach enhances
the model's resilience in the face of class imbalance. Second, recognizing the
quadratic complexity of dynamic routing leading to training inefficiencies, we
adopt the attention mechanism as a more computationally efficient alternative.
Our results demonstrate that CardioCaps surpasses traditional machine learning
baseline methods, including Logistic Regression, Random Forest, and XGBoost
with sampling methods and a class weight matrix. Furthermore, CardioCaps
outperforms other deep learning baseline methods such as CNNs, ResNets, U-Nets,
and ViTs, as well as advanced CapsNets methods such as EM-CapsNets and
Efficient-CapsNets. Notably, our model demonstrates robustness to class
imbalance, achieving high precision even in datasets with a substantial
proportion of negative cases.

通过使用动态路由神经胶囊网络（DR-CapsNets）和注意力机制，本论文提出了一种名为 CardioCaps 的新型 DR-CapsNet 架构，用于不平衡分类的超声心动图分类，该模型在面对类别不均衡的情况下具有较高的鲁棒性，同时胜过传统的机器学习方法和其他深度学习方法。

CardioCaps：基于注意力的胶囊网络用于类别不平衡的心脏超声图分类

CardioCaps: Attention-based Capsule Network for Class-Imbalanced  Echocardiogram Classification

Capsule neural networks replace simple, scalar-valued neurons with
vector-valued capsules. They are motivated by the pattern recognition system in
the human brain, where complex objects are decomposed into a hierarchy of
simpler object parts. Such a hierarchy is referred to as a parse-tree.
Conceptually, capsule neural networks have been defined to realize such
parse-trees. The capsule neural network (CapsNet), by Sabour, Frosst, and
Hinton, is the first actual implementation of the conceptual idea of capsule
neural networks. CapsNets achieved state-of-the-art performance on simple image
recognition tasks with fewer parameters and greater robustness to affine
transformations than comparable approaches. This sparked extensive follow-up
research. However, despite major efforts, no work was able to scale the CapsNet
architecture to more reasonable-sized datasets. Here, we provide a reason for
this failure and argue that it is most likely not possible to scale CapsNets
beyond toy examples. In particular, we show that the concept of a parse-tree,
the main idea behind capsule neuronal networks, is not present in CapsNets. We
also show theoretically and experimentally that CapsNets suffer from a
vanishing gradient problem that results in the starvation of many capsules
during training.

本研究提出胶囊神经网络的概念，并通过对 CapsNet 的分析，发现它在理论和实践中都存在解决不了的问题，导致无法在大规模数据集上实现。