  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.
