Dynamic Data Processing Model Selection for Medical Imaging
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Solution Overview
Problem
Existing data processing methods often produce inaccurate results when using the same processing model for different data sets due to the greater specificity of the data, particularly in medical imaging where variations in patient body type and scan parameters can lead to inaccurate image reconstruction and artifact correction.
Innovation Solution
A method is provided to determine a target data processing model by obtaining a data set, processing it using an evaluation model to obtain evaluation results for multiple candidate models, and selecting the most suitable model based on these results, which can include characteristic data and user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same data processing model is used to process different data sets, then the processing efficiency is maintained, but the processing accuracy deteriorates due to data specificity
Solution Approach 1:
The patent implements dynamic model selection where the data processing model is not fixed but dynamically adjusted based on the characteristics of the input data. The system evaluates data characteristics (such as data type, complexity, and domain) and selects the most appropriate model from a plurality of candidate models, allowing the processing approach to adapt to different data specifics while maintaining efficiency through automated selection
2Measurement precision
If multiple candidate data processing models are evaluated and selected based on data characteristics, then the processing accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary evaluation mechanism that acts as a mediator between the data characteristics and the model selection process. This evaluation module analyzes data characteristics and provides guidance for model selection, simplifying the overall system architecture by separating the evaluation function from the model execution function. The intermediary layer handles the complexity of multiple model evaluations centrally, reducing the burden on individual processing components
Data Source
AI summary
Systems and methods for determining a target data processing model is provided. The methods may include obtaining a data set including data to be processed by a target data processing model; processing the data set using an evaluation model to obtain an evaluation result for each of a plurality of candidate data processing models, the evaluation model being a trained machine learning model; and determining, from the plurality of candidate data processing models, the selected data processing model based on the evaluation results.