Core Concepts
CardioCaps introduces a novel attention-based DR-CapsNet architecture for class-imbalanced echocardiogram classification, surpassing traditional and deep learning methods.
Abstract
CardioCaps is a novel model designed to address the challenges of class-imbalanced echocardiogram classification. It incorporates a weighted margin loss with an auxiliary loss function based on the Ejection Fraction regression task. The attention mechanism replaces dynamic routing for enhanced training efficiency. Results show superior performance compared to traditional machine learning and deep learning methods, demonstrating robustness in datasets with substantial negative cases.
Stats
CardioCaps achieves an accuracy of 85% on an echocardiogram dataset.
The dataset comprises 80% normal and 20% abnormal echocardiograms.
CardioCaps outperforms Logistic Regression, Random Forest, XGBoost, CNNs, ResNets, U-Nets, ViTs, EM-CapsNets, and Efficient-CapsNets.
Quotes
"Our results demonstrate that CardioCaps surpasses traditional machine learning baseline methods."
"CardioCaps outperforms other deep learning baseline methods such as CNNs, ResNets, U-Nets, and ViTs."
"Our model demonstrates robustness to class imbalance."