Cell Staining Intensity Detection for Consistent Digital Pathology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional pathological diagnostic methods are time-consuming and prone to subjectivity in interpreting cell staining intensity, particularly in digital pathology, leading to inconsistent diagnosis results.
Innovation Solution
A method for detecting cell-by-cell staining intensity using a staining intensity detection model that classifies staining into multiple classes, trained on labeled data sets, utilizing a self-supervised learning-based feature extraction model, and allows user-adjustable confidence value bias for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional visual inspection method is used, then diagnostic accuracy can be maintained through expert judgment, but diagnostic efficiency is low and time consumption is high
Solution Approach 1:
The patent replaces the mechanical visual inspection system with a digital image processing system using deep learning models. The staining intensity detection model automatically analyzes digital pathology images, substituting the manual mechanical process of visual inspection with an automated computational system that processes images digitally to determine staining intensity classes.
Solution Approach 2:
The system enables self-service diagnosis by providing automated staining intensity classification without requiring continuous expert intervention. The deep learning model independently processes images and generates diagnostic classifications, allowing the system to serve itself in performing the diagnostic function that previously required pathologist expertise.
2Productivity
If automated digital pathology analysis is introduced, then diagnostic efficiency is improved, but measurement precision of staining intensity decreases due to subjectivity
Solution Approach 1:
The patent transforms the subjective visual assessment parameter into an objective computational parameter by using deep learning models to classify staining intensity into discrete categories (negative, weak, moderate, strong). This parameter change from subjective judgment to standardized classification improves measurement precision while maintaining automated efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where the staining intensity detection model is trained on labeled data with ground truth annotations. This feedback loop allows the model to learn from correct examples and continuously improve its measurement precision, ensuring that automated analysis achieves high accuracy in staining intensity classification.
3Measurement precision
If manual cell-by-cell analysis is performed, then measurement precision of staining intensity is maintained, but productivity is low and time loss is high
Solution Approach 1:
The patent segments the diagnostic process into distinct automated stages: image digitization, cell detection, staining intensity classification, and result generation. This segmentation allows each component to be optimized independently, with the staining intensity detection model focusing specifically on intensity classification while other components handle image processing and analysis, thereby improving overall productivity without sacrificing precision.
4Speed
If digital pathology system is implemented, then diagnostic speed is improved, but device complexity increases due to infrastructure requirements
Solution Approach 1:
The staining intensity detection model is designed with multi-functionality to handle various staining types and tissue samples through a single unified system. The model can process different immunohistochemical stains and tissue types, reducing the need for multiple specialized systems and thereby decreasing overall device complexity while maintaining high diagnostic speed.
Data Source
AI summary
According to one embodiment of the present disclosure, a method of detecting cell-by-cell staining intensity from a pathological image composed of a tissue slide image may include: extracting a bounding box including a detection target cell from the tissue slide image; inferring a staining intensity class of the bounding box based on a trained staining intensity detection model; and displaying bounding boxes inferred to be different staining intensity classes in different ways.