Phenotype Prediction Device Using Optimized Imaging Descriptor Selection
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Solution Overview
Problem
Prediction devices face the 'curse of dimensionality' when dealing with large input data, leading to poor performance in predicting phenotypes from imaging data, as they struggle to find a balance between complexity and adjustment to the data.
Innovation Solution
A method that involves acquiring multidimensional images, extracting and classifying descriptors, selecting the most relevant descriptors, and constructing a prediction device using a combination of preprocessing, univariable methods, and calibration techniques to optimize the predictive function, addressing the curse of dimensionality by finding a balance between complexity and data adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all available imaging descriptors are used in the prediction device, then the completeness of information is improved, but the device complexity and computational burden increase significantly due to the curse of dimensionality
Solution Approach 1:
The patent extracts only the most relevant descriptors from the large set of available imaging descriptors. A selection process identifies and extracts a reduced subset of descriptors that are most predictive of the phenotype, discarding redundant or less informative descriptors. This extraction principle resolves the contradiction by maintaining information completeness through selective retention of critical features while reducing overall complexity.
Solution Approach 2:
The patent segments the large set of imaging descriptors into multiple subsets based on their relevance to different phenotypic characteristics. By dividing the descriptor space into meaningful segments or groups, the system can process and evaluate descriptors in manageable portions, reducing computational burden while systematically identifying the most informative descriptors for prediction.
2Measurement precision
If a large number of descriptors are used to improve prediction accuracy, then the prediction performance is improved, but the risk of over-learning and poor generalization increases
Solution Approach 1:
The patent applies partial action by using only a subset of available descriptors rather than all descriptors. By selectively applying a portion of the available information (the most relevant descriptors), the system achieves sufficient prediction accuracy without the harmful effects of using excessive descriptors that would lead to over-learning and poor generalization to new data.
Solution Approach 2:
The patent changes the parameter of descriptor quantity from using all available descriptors to using an optimized subset. By adjusting this parameter based on relevance analysis and performance optimization, the system finds the optimal balance between prediction accuracy and generalization capability, avoiding both under-fitting and over-fitting scenarios.
3Device complexity
If the prediction device is simplified by reducing the number of descriptors, then the device complexity is reduced, but the prediction performance deteriorates due to loss of important information
Solution Approach 1:
The patent applies local quality by ensuring that the reduced set of descriptors maintains high predictive quality in the critical local regions of the descriptor space. Rather than uniformly reducing all descriptors, the system identifies and retains descriptors that locally contribute most to prediction accuracy in specific phenotypic contexts, ensuring that simplification does not compromise essential predictive information.
Data Source
AI summary
A method for developing a prediction device for predicting a phenotype of a person from imaging data of the person is provided. The method includes determining imaging descriptors, the determining imaging descriptors including acquiring multidimensional images of people with an imaging apparatus and extracting multidimensional image elements from the acquired multidimensional images to serve as descriptors The method also includes classifying the predetermined descriptors on the basis of the capability thereof to predict the phenotype, selecting, from among the classified descriptors, a relevant number of the best-classified descriptors that is sufficient to predict the phenotype and constructing the prediction device from the selected descriptors.