Cytology AI Model for Multi-Stain Cancer Classification
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Solution Overview
Problem
Existing cell staining methods in cytology present challenges for developing accurate artificial intelligence models due to differences in staining colors, requiring separate models for each method and leading to incorrect learning and low sensitivity in cancer diagnosis.
Innovation Solution
A specimen cytology supporting device and method that extracts tile images from cytology slide images using a pre-processor and classifies cancer types using a prediction model with annotation-based learning, allowing for accurate diagnosis across various cell staining methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate artificial intelligence models are developed for each cell staining method, then the model can learn specific staining characteristics, but the overall diagnostic accuracy decreases due to incorrect learning of staining method differences and low sensitivity in cancer diagnosis
Solution Approach 1:
The patent applies universality by developing a single artificial intelligence model that can handle multiple cell staining methods (H&E, Pap, Diff-Quik) simultaneously. The model is trained to recognize and adapt to different staining characteristics without requiring separate models for each method, thereby improving diagnostic accuracy while reducing model complexity and the need for multiple separate systems
Solution Approach 2:
The patent utilizes parameter changes by incorporating staining method identification as an additional parameter in the model's decision-making process. The model dynamically adjusts its analysis based on the detected staining method, changing its internal parameters and thresholds to optimize performance for each specific staining type rather than using fixed parameters across all methods
2Adaptability or versatility
If various cell staining methods are used according to different situations, then the versatility of cytology diagnosis is improved, but the artificial intelligence model learning becomes incorrect due to significant color differences between methods
Solution Approach 1:
The patent applies local quality by training the artificial intelligence model to recognize and adapt to the specific characteristics of each staining method locally. Instead of treating all images uniformly, the model identifies the staining method present in each image and adjusts its analysis parameters, feature extraction weights, and classification thresholds specifically for that staining type, thereby maintaining high learning accuracy across diverse staining methods
Solution Approach 2:
The patent introduces staining method identification as an intermediary step between image input and cancer classification. This intermediary component detects which staining method is used and provides this information to the main classification model, which then uses it to adjust its processing. This mediator enables the system to handle various staining methods accurately without requiring separate models for each
Data Source
AI summary
A device and method for extracting a plurality of tile images from specimen cytology slide images divided according to a cell staining method, and classifying a class of at least one of a type of cancer and whether there is cancer according to a cell staining method in any specimen cytology slide image using a prediction model that has undergone annotation-based learning on the specimen cytology slide images or the tile images.