Immunohistochemistry Staining for AI Training Data Generation
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Solution Overview
Problem
Current pathology AI software relies heavily on time-consuming and labor-intensive data training using H&E staining and morphology, which requires senior pathologists and is inefficient, especially when dealing with large volumes of samples and a shortage of available pathologists.
Innovation Solution
A method for generating training data based on immunohistochemistry, involving immunohistochemical staining with different antibodies and subsequent labeling, allowing for the rapid and accurate generation of training data without the need for H&E slicing and histomorphology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If H&E staining and morphology-based data training is used for pathology AI software, then the training data can be generated using conventional methods, but the process requires large amounts of labeling work by senior pathologists, leading to high labor cost and low efficiency
Solution Approach 1:
The patent applies self-service by enabling the staining system to automatically generate labeled training data without requiring senior pathologists to perform manual labeling. The immunohistochemistry staining process inherently provides the labeling information through specific antibody binding patterns, allowing the system to label itself rather than relying on human experts for each labeling task
Solution Approach 2:
The patent replaces the mechanical system of manual pathologist labeling with an automated immunohistochemistry-based labeling system. Instead of pathologists visually examining H&E stained slides and manually annotating features, the system uses specific antibodies that automatically highlight target structures, replacing human cognitive and manual work with automated biochemical detection
2Quantity of substance
If H&E staining and morphology-based data training is used for pathology AI software, then the training dataset can be formed through manual labeling, but the process is time-consuming and affects subsequent AI learning progress
Solution Approach 1:
The patent applies preliminary action by pre-labeling training data through immunohistochemistry staining before AI model training begins. The staining process permanently marks target structures with specific colors, creating pre-labeled datasets that are ready for immediate AI learning without requiring time-consuming post-staining pathologist labeling
Solution Approach 2:
The patent enables continuous generation of labeled training data by implementing a streamlined immunohistochemistry workflow that eliminates idle time between staining and labeling. The automated detection system continuously processes stained slides and generates labeled data without interruption, maintaining continuous productive action throughout the data preparation process
3Measurement precision
If senior pathologists perform labeling work on H&E stained slices, then accurate labeling can be achieved, but the challenge of inadequate quantity of senior pathologists leads to inability to complete labeling work in time
Solution Approach 1:
The patent applies self-service by enabling the staining system to automatically generate labeled training data without requiring senior pathologists to perform manual labeling. The immunohistochemistry staining process inherently provides the labeling information through specific antibody binding patterns, allowing the system to label itself rather than relying on human experts for each labeling task
Solution Approach 2:
The patent changes the fundamental parameter of how labeling information is obtained. Instead of relying on pathologist expertise and manual annotation, the system changes to using antibody-specific binding as the labeling mechanism. This parameter change from human-cognitive labeling to biochemical-specific labeling enables automated, high-throughput accurate labeling
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enables pathologists to label tissues or cells quickly and accurately using immunohistochemistry, reducing the workload and improving efficiency compared to traditional methods, while providing more accurate data for AI training.
Implementation Method 1
performing immunohistochemical staining on a target object through different antibodies
Implementation Method 2
developing color with a freshly prepared AP-Red color developing solution
Data Source
AI summary
The present invention relates to the technical field of computers, and more particularly, to a method for generating training data based on immunohistochemistry, and a storage device. The method for generating the training data based on immunohistochemistry includes the following steps: performing immunohistochemical staining on a target object through different antibodies; labeling the target object according to staining results; and generating training data according to labeling results. According to the method, a pathologist can label a tissue or cell of interest rapidly and conveniently without performing labeling through H&E slicing in combination with histomorphology. In addition, since this tissue or cell is labeled based on an immunohistochemistry technology of a gold standard, this labeling is more accurate compared with manual labeling performed through H&E slicing in combination with histomorphology.