Synthetic Medical Image Generation for Model Refinement
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Solution Overview
Problem
Machine learning models for medical image processing face limitations in inferencing capability due to the scope of training data, and updating these models post-deployment is challenging due to contractual restrictions and privacy issues related to sharing deployment site patient imaging data.
Innovation Solution
A system that generates synthetic medical images from non-image data feedback, allowing for the refinement and updating of medical image inferencing models without requiring actual image data, using a processor and memory to execute components that include an image generation component and a refinement component, which updates the model using synthetic images generated from text-based feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual deployment site patient imaging data is obtained for model refinement, then model performance improvement is achieved, but data sharing restrictions and privacy issues arise
Solution Approach 1:
The patent generates synthetic copies of medical images that replicate the statistical properties and failure modes of real deployment data without using actual patient images. These synthetic images serve as proxies for real data, enabling model refinement while completely avoiding data sharing restrictions and privacy concerns.
Solution Approach 2:
The patent introduces an intermediary synthetic image generation system that translates real-world failure modes into synthetic training data. This intermediary layer allows the model to learn from deployment environment characteristics without direct access to sensitive patient data, bridging the gap between restricted real data and model improvement needs.
2Measurement precision
If model retraining is performed continuously to improve inferencing capability, then model accuracy increases, but training data scope limitations persist
Solution Approach 1:
The patent creates a dynamic synthetic data generation system that adapts to new failure modes as they are discovered in the deployment environment. The system continuously evolves the synthetic training data to match emerging performance gaps, allowing the model to improve indefinitely without being constrained by the static scope of original training data.
Solution Approach 2:
The patent performs preliminary analysis of deployment environment failure modes and pre-generates synthetic training data targeting these specific weaknesses before model retraining. This preliminary preparation ensures that each retraining cycle addresses the most critical performance gaps, maximizing the efficiency and effectiveness of continuous model improvement.
3Object-affected harmful factors
If non-image feedback data is used to generate synthetic images, then data privacy is protected, but feedback information completeness may be reduced
Solution Approach 1:
The patent transforms non-image feedback parameters (such as performance metrics, failure mode classifications, and quality scores) into comprehensive synthetic image generation parameters. By changing the parameter representation from direct image data to derived feedback attributes, the system protects privacy while recovering complete training information through mathematical transformations of the feedback data.
Data Source
AI summary
Techniques are described for refining or updating medical image inferencing models post deployment using synthetic images generated from non-image data feedback. In an example, a system can comprise a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components can comprise an image generation component that generates synthetic medical images based on feedback information associated with performance of a medical image inferencing model received in association with application of the medical image inferencing model to medical images in a deployment environment, wherein the feedback information excludes image data. The computer-executable components can further comprise a refinement component that updates the medical image inferencing model using the synthetic images and a model updating process.