Medical Image ML Models Using Explicit Equipment Parameters
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Solution Overview
Problem
Existing machine learning models for medical image analysis are inaccurate when applied to images captured with different equipment parameters, leading to excessive resource consumption and reduced performance due to implicit consideration of equipment parameters.
Innovation Solution
A machine learning model is trained to explicitly receive equipment parameters as input, allowing it to learn how variations in these parameters affect inferencing tasks, thereby maintaining accuracy across different imaging device configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for medical image analysis, then device complexity is reduced, but measurement precision deteriorates when applied to images from different equipment parameters
Solution Approach 1:
The patent applies parameter changes by incorporating equipment parameters (such as scanner model, protocol type, field of view, matrix size, slice thickness) as additional input features to the machine learning model. This allows the model to adapt its inferencing based on the specific imaging conditions, thereby maintaining high measurement precision across diverse equipment configurations without requiring multiple separate models.
2Measurement precision
If multiple machine learning models are trained for different equipment parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements universality by designing a single machine learning model that can handle multiple equipment parameter configurations. The model accepts equipment parameters as input features and adjusts its inferencing accordingly, making it universally applicable across different imaging devices and protocols. This eliminates the need to train and deploy multiple separate models, thereby reducing device complexity while maintaining precision.
3Ease of operation
If equipment parameters are implicitly considered in the model, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent uses equipment parameters as an intermediary between the imaging device and the machine learning model. These parameters serve as explicit mediators that convey information about imaging conditions to the model, enabling precise adjustments in inferencing. This approach maintains ease of operation since the parameters are automatically provided by the imaging system, while simultaneously improving measurement precision through explicit consideration of equipment variations.
Data Source
AI summary
Systems/techniques that facilitate machine learning image analysis based on explicit equipment parameters are provided. In various embodiments, a system can access a medical image generated by a medical imaging device. In various instances, the system can perform, via execution of a machine learning model, an inferencing task on the medical image. In various cases, the machine learning model can receive as input the medical image and a set of equipment parameters. In various aspects, the set of equipment parameters can indicate how the medical imaging device was configured to generate the medical image.