Trained Model Evaluation System for Medical Image Analysis
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Solution Overview
Problem
Existing methods for evaluating trained models lack a standardized approach to determine their effectiveness, as standards for determining cell viability or lesion recognition can vary between users, making it difficult to assess whether a trained model is suitable for a specific user's needs.
Innovation Solution
A system and method that acquires an examination image, applies it to multiple trained models, generates result images showing predicted positive or negative regions, and displays these images alongside the corresponding models, allowing users to evaluate and select models based on their specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple trained models are developed to handle different evaluation standards, then the coverage of evaluation criteria is improved, but the complexity of model selection and evaluation increases
Solution Approach 1:
The evaluation system is segmented into multiple independent trained models, each specialized in evaluating a specific criterion (e.g., cell viability, lesion recognition). Each model processes images independently according to its specific evaluation standard, allowing the system to handle diverse evaluation criteria through modular components rather than a monolithic complex system.
Solution Approach 2:
The evaluation assistance system serves multiple functions by accommodating various trained models with different evaluation criteria within a single unified platform. The system can evaluate images based on different standards (cell viability, lesion detection, differentiation state) using the same user interface and processing framework, making the system universally applicable to diverse evaluation needs without requiring separate specialized systems for each criterion.
2Measurement precision
If standardized evaluation methods are implemented, then the objectivity of model evaluation is improved, but the flexibility to accommodate user-specific criteria is reduced
Solution Approach 1:
The system achieves objectivity through standardized evaluation parameters and criteria while maintaining flexibility by allowing users to select and configure which criteria to apply. Each trained model uses defined, objective parameters for evaluation (e.g., specific thresholds for cell viability or lesion detection), ensuring consistent and unbiased assessment. Users can customize their evaluation by selecting different models or adjusting parameters within the standardized framework, thus maintaining both objectivity and adaptability.
Solution Approach 2:
The evaluation system incorporates feedback mechanisms that allow users to review evaluation results, compare different models' assessments, and adjust their selections based on the feedback. Users can see how different trained models evaluate the same image according to different criteria, providing feedback loops that help users understand the implications of each evaluation standard and make informed decisions about which model best suits their specific needs while maintaining objective, standardized assessment procedures.
3Reliability
If trained models are evaluated using multiple criteria, then the comprehensiveness of evaluation is improved, but the time required for evaluation increases
Solution Approach 1:
The evaluation process is segmented into parallel independent tasks, with each trained model evaluating a specific criterion simultaneously. Instead of sequentially checking multiple criteria through a single model, the system runs multiple specialized models in parallel, each processing the image independently according to its specific criterion. This segmentation enables comprehensive multi-criteria evaluation without linearly increasing evaluation time, as parallel processing completes all assessments concurrently.
Solution Approach 2:
The system performs preliminary actions by pre-training and pre-configuring multiple specialized models with different evaluation criteria before actual evaluation is needed. These models are ready to process images immediately according to their specific criteria, eliminating the need for time-consuming real-time training or configuration during evaluation. The preliminary preparation of multiple evaluation models allows for rapid, comprehensive assessment when images need to be evaluated against multiple criteria.
Data Source
AI summary
An evaluation assistance method includes: acquiring a first image to be used for performance evaluation of trained models; generating a plurality of second images, each of the plurality of second images being a result of processing the first image by each of a plurality of trained models; and displaying each of the plurality of trained models in association with a corresponding second image of the plurality of second images.