Histology Sample Matching With Confidence-Scored Image Analysis
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Solution Overview
Problem
Conventional imaging systems struggle with accurately matching images of histological samples, leading to potential misdiagnosis and inefficiencies due to user errors and lack of verification mechanisms.
Innovation Solution
An imaging system utilizing an image analysis module, such as a convolutional neural network, to analyze image data from histological samples, incorporating identifiers and machine learning algorithms to determine the likelihood of matches and provide confidence-based recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging systems are used to match histological sample images, then the matching process can be performed, but the accuracy is poor and user errors occur frequently
Solution Approach 1:
The system implements feedback by providing confidence scores for each match recommendation and allowing users to verify or correct matches. The machine learning model continuously learns from user corrections to improve future matching accuracy, creating a closed-loop feedback system that enhances both precision and reliability over time.
Solution Approach 2:
The patent replaces manual image matching (mechanical/systematic human operation) with an automated machine learning-based image analysis system. This substitution eliminates human error in the matching process while maintaining high accuracy through algorithmic pattern recognition and confidence-based recommendations.
2Productivity
If manual matching methods are used, then users can perform the matching, but the process is time-consuming and prone to errors
Solution Approach 1:
The system performs self-service by automatically generating match recommendations with confidence scores without requiring manual intervention for each comparison. The machine learning model independently processes images, generates matches, and provides confidence assessments, freeing users from time-consuming manual verification while maintaining high productivity.
Solution Approach 2:
The system performs preliminary action by pre-processing images, extracting features, and generating match recommendations before user review. This preliminary automated processing significantly reduces the time users need to spend on verification while maintaining high matching efficiency through pre-computed confidence scores.
3Productivity
If automated matching algorithms are implemented, then matching speed increases, but the accuracy and reliability of matches decrease
Solution Approach 1:
The system changes parameters by using multiple image features (color, texture, shape, intensity) and adjusting the confidence threshold based on the specific matching task. The machine learning model dynamically weights different features and adjusts decision parameters to maintain high accuracy while processing images rapidly, resolving the trade-off between speed and precision.
Solution Approach 2:
The patent employs a composite approach by combining multiple image analysis techniques (feature extraction, machine learning, confidence scoring) into an integrated system. This composite methodology maintains high processing speed while achieving superior match accuracy through the synergistic combination of multiple analytical components.
Data Source
AI summary
Features are disclosed for imaging block and slice samples using an imaging system. The imaging system can link the images by identifiers associated with the block and slice samples. The imaging system can train a machine learning algorithm based on correctly linked images. In some embodiments, the trained machine learning algorithm may include an image analysis module or a convolutional neural network. The imaging system can use the trained machine learning algorithm in order to determine a confidence score of a match between the block and the slice samples. The trained machine learning algorithm can use features of the block and the slice samples such as shape and tissue morphology to determine whether the samples match. In some embodiments, when the confidence score is below a certain threshold, the imaging system can alert a user that the samples may not match.