Neural Network Objective Function Segmentation for ECG Anomaly Detection
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Solution Overview
Problem
Conventional neural network models face challenges in achieving accurate and interpretable image classification due to the lack of comprehensive understanding of how auxiliary information improves model accuracy and trustworthiness, often compromising standard accuracy for adversarial robustness.
Innovation Solution
The implementation of a neural network system with a modified objective function that employs different degrees of regularization for features inside and outside bounding boxes, utilizing gradient-based penalties and interactive deep learning to enhance model accuracy, robustness, and interpretability, specifically for image and ECG signal classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural network models are trained with standard objective functions, then they achieve basic classification performance, but they lack interpretability and robustness against adversarial examples
Solution Approach 1:
The objective function is segmented into multiple components: a standard classification loss term and an additional regularization term. This segmentation allows the model to maintain basic classification performance while adding interpretability constraints through the separate regularization component, resolving the contradiction between robustness and complexity.
Solution Approach 2:
The regularization term applies local quality constraints by encouraging smoothness of gradients with respect to input perturbations in specific regions. This local control over gradient behavior enhances adversarial robustness without requiring complete redesign of the objective function, thus improving reliability without excessive complexity.
2Reliability
If neural networks prioritize adversarial robustness, then they become more resistant to malicious inputs, but standard classification accuracy is compromised
Solution Approach 1:
The patent merges the standard classification loss with the adversarial robustness regularization term into a unified objective function. This combination allows the model to simultaneously optimize for both classification accuracy and robustness, resolving the trade-off by integrating both goals into a single optimization process rather than treating them as conflicting objectives.
Solution Approach 2:
The regularization strength is controlled by a hyperparameter that can be adjusted to balance the trade-off between classification accuracy and adversarial robustness. By changing this parameter, the system can adapt to different requirements, achieving both high accuracy and robustness under appropriate conditions.
3Ease of operation
If neural networks are trained to improve interpretability, then they provide more understandable decision boundaries, but model accuracy may be reduced
Solution Approach 1:
The regularization term provides feedback during training by penalizing models with rough or non-smooth decision boundaries. This feedback mechanism guides the optimization process toward solutions that are both accurate and interpretable, as the smoothness constraint encourages simpler, more understandable decision boundaries without significantly compromising accuracy.
Data Source
AI summary
Methods, apparatus and systems for robust and accurate detection of anomalies in medical images and electrocardiograms are disclosed. One example system for training a neural network engine includes a processor that is configured to receive a set of training electrocardiogram signals. At least one electrocardiogram signal in the set of training electrocardiogram signals is associated with metadata identifying a region of interest that includes a heart anomaly. The processor is configured to input the set of training electrocardiogram signals into the neural network engine. The neural network engine is trained using an objective function having a first regularization parameter and a second regularization parameter. The processor is also configured to operate the neural network engine to identify the heart anomaly by classifying the set of training electrocardiogram signals and adjust the neural network engine based on the identified heart anomaly and the metadata.