Medical Image Model Performance Estimation Using Metadata
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for measuring the performance of computer vision ML models on medical images are resource-intensive, time-consuming, and cannot predict performance without access to the images, especially in new deployments or when data drift occurs.
Innovation Solution
A performance estimation ML model that predicts the performance of computer vision ML models using metadata parameters, such as imaging device properties and patient attributes, without the need to execute the said technical solutions, and their actual contribution to solving the technical problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to measure performance of computer vision ML models on medical images, then accurate performance measurement is achieved, but resource consumption increases and time required increases
Solution Approach 1:
The patent creates a performance estimation model that is trained on metadata from actual model executions. Once trained, this estimation model can predict performance metrics without requiring actual execution of the computer vision ML model on medical images. This copying approach replaces resource-intensive actual executions with lightweight predictions based on metadata, thereby reducing resource consumption while maintaining performance measurement capability
Solution Approach 2:
The patent performs preliminary actions by training the performance estimation model in advance using metadata from actual model executions. This preliminary training phase captures the relationship between metadata and performance metrics. After training, the estimation model can quickly predict performance for new cases without requiring actual model execution, thus reducing time and resource requirements for subsequent performance measurements
2Measurement precision
If existing methods are used to measure performance of computer vision ML models on medical images, then accurate performance measurement is achieved, but time required increases
Solution Approach 1:
The performance estimation model creates a lightweight copy of the performance measurement capability that operates on metadata alone. This eliminates the time-consuming step of actually executing the computer vision ML model on medical images, while still providing accurate performance predictions based on the patterns learned during training
Solution Approach 2:
By performing the complex analysis work during the preliminary training phase, the system establishes a ready-to-use estimation model. Subsequent performance measurements only require feeding metadata into this pre-trained model, dramatically reducing the time required for performance assessment compared to actual model execution
3Reliability
If computer vision ML model is executed to predict performance, then performance metric is obtained, but resource utilization increases
Solution Approach 1:
The patent extracts the essential performance prediction capability from the full computer vision ML model execution process. By isolating and training a separate performance estimation model on metadata alone, it separates the performance assessment function from the actual image analysis function, enabling reliable performance prediction without the high resource cost of full model execution
Solution Approach 2:
The performance estimation model serves as a lightweight copy that replicates the performance prediction capability of the full computer vision ML model. This copy operates exclusively on metadata and provides reliable performance metrics without requiring the computational resources needed for actual image processing
4Reliability
If performance estimation ML model is trained on training dataset, then performance prediction capability is improved, but training time and resources are required
Solution Approach 1:
The patent changes the input parameters for performance estimation from actual medical images to metadata only. This parameter change fundamentally reduces the complexity and resource requirements of both training and inference, while maintaining the ability to predict model performance accurately based on the patterns in metadata
Data Source
AI summary
There is provided a method, comprising: feeding target metadata parameters of target medical image(s) into a performance estimation machine learning (ML) model, and obtaining a performance metric of a computer vision ML model as an outcome of the performance estimation ML model, wherein the performance estimation ML model is trained on a training dataset comprising records, wherein a record is created by feeding a sample medical image into the computer vision ML model, obtaining an outcome of the computer vision ML model, obtaining a ground truth of the sample medical image corresponding to the outcome, extracting metadata parameters associated with the sample medical image, and wherein the record includes metadata parameters associated with the sample medical image and a ground truth label comprising a performance metric of the computer vision ML model computed based on the ground truth of the sample medical image and the outcome.