Machine Learning Model for Digital Pathology Specimen Classification
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Solution Overview
Problem
Existing systems face challenges in accurately classifying tissue specimens from digital pathology images, often due to missing or incorrect specimen type labels, which can lead to errors in diagnosis and treatment.
Innovation Solution
The implementation of a machine learning model that analyzes digital pathology images to determine specimen characteristics, such as type and quality, without relying on laboratory information system (LIS) data, using a dataset of training images to predict specimen properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If specimen type labels are manually input into LIS, then the system can store and access tissue classification information, but errors and inaccuracies increase due to manual input mistakes
Solution Approach 1:
The system enables self-service by allowing the digital pathology image and specimen data to automatically generate and verify their own classification labels through machine learning models, eliminating dependence on manual LIS input and reducing human error in specimen type classification
Solution Approach 2:
The system implements feedback by using machine learning models to analyze digital pathology images and compare predicted specimen types against LIS-stored labels, identifying and flagging discrepancies for correction, thereby continuously improving classification accuracy through iterative verification
2Adaptability or versatility
If third party access to anonymized images is provided without LIS data, then data sharing and research access improve, but specimen type information is lost or unavailable
Solution Approach 1:
The system creates a functional copy of the specimen type information by using machine learning models to predict and generate classification labels directly from anonymized digital pathology images, allowing third parties to access both image and classification data without needing original LIS access or compromising patient privacy
Solution Approach 2:
The system segments the classification function from the LIS database by implementing independent machine learning models that can derive specimen type information directly from image content, enabling data sharing scenarios where images are accessed without corresponding LIS metadata
3Productivity
If machine learning models are used to classify specimens, then automation and speed improve, but system complexity and computational requirements increase
Solution Approach 1:
The system applies preliminary action by pre-training machine learning models on extensive datasets of digital pathology images with known specimen types before deployment, so that during actual use the models can rapidly classify new specimens without requiring complex real-time processing or extensive computational resources during classification operations
Data Source
AI summary
Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.