Machine Learning Pathological Image Analysis for PD-L1 Quantification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing pathological images, particularly for predicting responsiveness to immunotherapy, are subjective and lack objective quantification due to manual counting of PD-L1 expression by humans.
Innovation Solution
A method and system using a machine learning model to analyze pathological images, detect objects associated with medical information such as tumor cells and immune cells, and calculate the level of PD-L1 expression in specific regions, providing objective and quantifiable analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual counting of PD-L1 expression is performed by humans, then the analysis can be performed with simple equipment, but the measurement precision and objectivity deteriorate due to subjective factors
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated image processing system that captures pathological images, segments cells based on staining characteristics, and automatically counts PD-L1 positive cells. This substitution eliminates subjective human factors while maintaining operational simplicity through automated workflows.
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the pathological sample and the final quantification result. This intermediary automatically performs cell segmentation, staining pattern recognition, and counting operations, providing objective measurements while keeping the overall process accessible and easy to implement.
2Measurement precision
If automated image processing is used to detect PD-L1 expression, then measurement precision and objectivity improve, but device complexity increases
Solution Approach 1:
The patent segments the complex image processing task into distinct modules: image capture, cell segmentation based on staining patterns, PD-L1 positive cell identification, and automated counting. Each module handles a specific function, reducing overall system complexity while maintaining high measurement precision through specialized processing at each stage.
3Device complexity
If manual counting by personnel is used, then device complexity remains low, but productivity decreases due to time-consuming manual analysis
Solution Approach 1:
The patent implements a self-service automated system where the image processing software independently performs cell identification, staining pattern recognition, and counting operations without requiring manual intervention. This automation dramatically increases productivity while keeping the system simple to operate through automated workflows that require minimal user input.
Data Source
AI summary
The present disclosure relates to a method, performed by at least one processor of an information processing system, of analyzing a pathological image. The method includes receiving a pathological image, detecting an object associated with medical information, in the received pathological image by using a machine learning model, generating an analysis result on the received pathological image, based on a result of the detecting, and outputting medical information about at least one region included in the pathological image, based on the analysis result.