Optical Performance Simulation for Diagnostic Model Robustness
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Solution Overview
Problem
Variations in mechanical components of imaging devices can adversely impact the accuracy of computer-aided diagnosis technologies, leading to operational hassles and limited robustness of diagnostic models.
Innovation Solution
A diagnostic platform that simulates the optical performance of imaging devices by predicting how light is transmitted, reflected, or transformed by the mechanical components, allowing for the optimization of diagnostic models prior to deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic models are trained on data from imaging devices with fixed mechanical components, then the models achieve high accuracy on those specific devices, but the models lack robustness when deployed on devices with component variations
Solution Approach 1:
The patent applies preliminary action by simulating mechanical component variations during the training phase. Training images are generated with simulated variations in lens focal length, sensor position, and other mechanical parameters before the actual diagnostic model training occurs. This allows the model to learn invariant features that are robust to mechanical variations, resolving the contradiction between high accuracy on specific devices and robustness across device variations.
Solution Approach 2:
The patent uses copying by creating synthetic training images that replicate the effect of mechanical component variations. Instead of requiring physical copies of multiple imaging devices, the system generates digital copies of training images with simulated mechanical variations applied. This enables the diagnostic model to be trained on diverse mechanical configurations without needing actual hardware variations, thereby improving robustness while maintaining accuracy.
2Adaptability or versatility
If multiple diagnostic models are trained for different imaging devices to account for mechanical variations, then the robustness improves, but the system complexity increases
Solution Approach 1:
The patent applies universality by developing a single diagnostic model training framework that handles multiple imaging device configurations. Instead of training separate models for each device, the system uses a universal training approach where synthetic images with various mechanical variations are generated from a single set of source images. This universal training set can then train one robust model that works across multiple devices, reducing system complexity while maintaining robustness.
Solution Approach 2:
The patent applies parameter changes by systematically varying mechanical parameters (lens focal length, sensor position, magnification) during synthetic image generation. Rather than creating entirely separate models for different devices, the system trains a single model on images with diverse parameter variations. This allows the model to learn across the parameter space, achieving robustness to mechanical variations without the complexity of maintaining multiple device-specific models.
3Measurement precision
If physical imaging devices are modified to reduce mechanical variations, then the diagnosis accuracy improves, but the manufacturing cost and complexity increase
Solution Approach 1:
The patent applies mechanics substitution by replacing physical mechanical precision requirements with computational compensation. Instead of modifying imaging devices to minimize mechanical variations through precision manufacturing, the system uses software-based synthetic image generation that explicitly models and accounts for mechanical variations. This substitutes mechanical precision requirements with computational processing, achieving high diagnosis accuracy without increasing manufacturing complexity or cost.
Data Source
AI summary
Introduced here are diagnostic platforms able to optimize computer-aided diagnostic (CADx) models by simulating the optical performance of an imaging device based on its mechanical components. For example, a diagnostic platform may acquire a source image associated with a confirmed diagnosis of a medical condition, simulate optical performance based on design data corresponding to a virtual prototype of the imaging device, generate a training image by altering the source image based on the optical performance, apply a diagnostic model to the training image, and then determine whether the performance of the diagnostic model meets a specified performance threshold. If the diagnostic model fails to meet the specified performance threshold, the diagnostic platform can automatically optimize the diagnostic model for the imaging device by altering its underlying algorithm(s).