Pathology Image Matching for Correct Patient Slide Association
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Solution Overview
Problem
In computational pathology, incorrectly associating pathology slides with the wrong patient can lead to incorrect diagnoses due to scanning and laboratory information system errors, compromising patient data integrity and diagnostic accuracy.
Innovation Solution
Utilizing machine learning techniques, particularly Siamese neural networks and triplet loss functions, to compare digital pathology images and determine if they match within a predetermined similarity threshold, ensuring correct patient association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification of slide-patient association is used, then accuracy can be maintained, but time consumption and labor requirements increase
Solution Approach 1:
The system performs self-verification by automatically comparing digital pathology images against stored reference images to determine patient association, eliminating the need for manual verification while maintaining accuracy
Solution Approach 2:
The patent replaces manual verification processes with an automated machine learning-based image comparison system that uses neural networks to determine slide-patient associations, substituting human labor with computational mechanisms
2Productivity
If automated image matching is implemented, then verification speed increases, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that simplifies the complexity of image comparison by learning patterns from reference images, allowing automated verification without requiring complex custom comparison algorithms
Solution Approach 2:
The system transforms the verification process by changing parameters through machine learning training, where the model learns to recognize and compare image features, converting complex pixel-by-pixel analysis into a simplified classification problem
3Measurement precision
If comprehensive image comparison is performed, then matching accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on reference images before actual verification, allowing the system to make accurate comparisons quickly without performing exhaustive analysis during the verification process itself
Solution Approach 2:
The system uses partial action by comparing only the most discriminative features learned during training rather than analyzing every pixel and detail, achieving sufficient accuracy for patient verification without the computational cost of exhaustive comparison
Data Source
AI summary
A computer-implemented method for processing electronic medical images, the method including receiving a plurality of electronic medical images of a medical specimen associated with a single patient. The plurality of electronic medical images may be inputted into to a trained machine learning system, the trained machine learning system being trained to compare each of the plurality of electronic medical images to each other to determine whether each pair of the electronic medical images matches within a predetermined similarity threshold. The trained machine learning system may output whether each pair of the electronic medical images matches within a predetermined similarity threshold. The output may be stored.