IHC Image Analysis Model for Reproducible Biomarker Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing immunohistochemically stained tissues face challenges in inter-reader agreement and reproducibility due to unclear criteria for distinguishing staining intensity, particularly between weak and moderate levels, leading to variable reading results.
Innovation Solution
A method and system that utilize a machine learning model trained with multiple staining intensity reference values to analyze immunohistochemically stained images, generating feature vectors based on pixel-level intensity comparisons, enabling precise biomarker expression analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual observation of tissue slides by pathologists is used to determine biomarker expression levels, then clinical diagnosis can be performed, but inter-reader agreement and reproducibility are poor due to unclear staining intensity criteria
Solution Approach 1:
The patent replaces the manual visual observation system with an automated machine learning-based image analysis system. The system uses trained neural networks to objectively quantify staining intensity and biomarker expression levels, eliminating subjective human interpretation. This substitution of mechanical/manual processes with automated computational processes directly resolves the contradiction by providing both precise measurements and high reliability through consistent, reproducible algorithmic evaluation.
Solution Approach 2:
The patent transforms the subjective qualitative assessment of staining intensity into objective quantitative parameters. By converting visual intensity ratings into numerical values through machine learning models, the system enables precise measurement of biomarker expression levels. The model outputs specific intensity scores and expression levels that are reproducible across different readers, resolving the reliability issue while maintaining measurement precision.
2Measurement precision
If multiple staining intensity reference values are used to train the machine learning model, then measurement precision of biomarker expression levels is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary training of the machine learning model using multiple staining intensity reference values before actual biomarker expression analysis. During this offline training phase, the model learns to distinguish different intensity levels by being exposed to diverse reference data. This preliminary preparation resolves the contradiction by embedding the complexity of handling multiple reference values into the trained model itself, allowing the deployed system to achieve high measurement precision without requiring complex real-time processing during actual analysis.
3Measurement precision
If pixel-level intensity comparison is performed to generate feature vectors, then analysis accuracy of biomarker expression is improved, but computational time and processing complexity increase
Solution Approach 1:
The patent segments the image analysis process into distinct stages: first generating pixel-level feature vectors through intensity comparison, then feeding these features into a trained machine learning model for final classification. This segmentation allows the computationally intensive pixel-level analysis to be performed once to extract essential features, while the model inference stage processes these features efficiently. This resolves the contradiction by concentrating computational effort in the feature extraction phase and enabling faster decision-making in the classification phase.
Data Source
AI summary
A method for training a machine learning model and a computing system performing same, wherein the machine learning model is trained to analyze biological tissue slide images stained by a immunohistochemical staining method for staining tissues expressing specific biomarkers and thus can be used to further elaborately analyze expression levels of biomarkers, etc., whereby a determination can be made on pathological specimen images by analyzing expression levels of biomarkers, etc. A method and system for training a machine learning model using training data, corresponding to immunohistochemically stained images, generated from multiple feature vectors calculated based on various staining intensity criteria.