Contour-Based Loss Function for ML Training
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Solution Overview
Problem
Existing machine learning (ML) model training methods rely on generic loss functions like Mean Squared Error (MSE), which fail to capture complex asymmetries in loss distributions, leading to suboptimal performance, especially in clinical applications where error significance varies complexly.
Innovation Solution
The method involves forming a loss function directly from a set of contour lines, allowing for the description of complex loss distributions. This approach accounts for clinical practice results and patient risk by using contour-based descriptions available in published literature, even when detailed 2D loss functions are not accessible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic loss functions like MSE are used, then the loss function is simple and easy to implement, but it fails to capture complex asymmetries in loss distributions
Solution Approach 1:
The patent transforms the loss function from a simple scalar value to a contour-based representation with multiple parameters. Each contour line represents a specific loss level, and the system uses a set of parameters including contour line coordinates, loss values, and asymmetry factors to accurately capture complex loss distributions while maintaining computational efficiency through parameterized modeling.
Solution Approach 2:
The patent introduces a new dimensional representation by plotting loss values on a 2D contour map with prediction error on one axis and truth value on the other. This dimensional transformation allows the loss function to capture complex asymmetries that cannot be represented by traditional 1D loss functions, while the contour lines provide an intuitive visual and computational framework.
2Reliability
If contour-based loss functions are used, then complex loss distributions and clinical significance are captured, but the loss function becomes more complex
Solution Approach 1:
The patent performs preliminary action by pre-defining contour lines and their associated loss values based on clinical knowledge and expert judgment before model training. This allows the complex loss distribution to be captured upfront through the contour structure, while the actual training process uses these pre-established contours to guide optimization, reducing the computational complexity during training iterations.
Solution Approach 2:
The contour lines serve as an intermediary between clinical knowledge and the machine learning model. Instead of directly incorporating complex clinical criteria into the loss calculation, the patent uses contour lines as a mediating structure that translates clinical significance into a computable form, simplifying the integration of domain knowledge while maintaining accuracy.
3Measurement precision
If asymmetric loss functions are used to account for different misclassification costs, then classification accuracy improves, but the loss function loses generality
Solution Approach 1:
The patent creates a universal loss function framework that can handle both symmetric and asymmetric cases through its contour-based structure. The contour lines can be configured to represent symmetric loss distributions when appropriate, or asymmetric distributions when needed, making the loss function adaptable to various problem types including classification, regression, and prioritized error correction scenarios.
Solution Approach 2:
The patent explicitly incorporates asymmetry into the loss function through asymmetric contour line configurations. By allowing contour lines to be positioned and shaped differently on either side of the diagonal (where prediction equals truth), the system can capture asymmetric loss distributions where errors in different directions have different clinical significance, while maintaining a unified mathematical framework.
Data Source
AI summary
Systems, apparatuses, and methods for training a machine learning (ML) model. Training the ML model may include using contour lines on a plot of prediction values to expected values to determine loss values indicative of errors between prediction values output by the ML model and corresponding expected values. The contour lines may be associated with loss values. Using the contour lines to determine the loss values may include, for each prediction value-expected value pair: generating a one-dimensional loss function through the prediction value-expected value pair, and using the one-dimensional loss function to determine a loss value for the prediction value-expected value pair. Training the ML model may include using an overall loss function to determine an overall loss of the ML model based on the determined loss values. Training the ML model may include adjusting the ML model to minimize the overall loss of the ML model.