Deep Learning Breast Tissue Density Classification
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Solution Overview
Problem
Current medical guidelines for breast tissue density assessment are subjective and lead to variability in classification, resulting in inconsistent recommendations for follow-up screening, as visual assessment methods are prone to errors and influenced by changing standards.
Innovation Solution
A deep convolutional model-based system for processing radiology images to classify breast tissue density into consistent classes, using a combination of convolutional neural networks and a patch model to improve accuracy and objectivity in identifying patients requiring further testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual assessment methods are used for breast tissue density classification, then the process is simple and quick, but the classification reliability and consistency deteriorate due to subjectivity and variability
Solution Approach 1:
The patent replaces the manual visual assessment system with an automated deep learning-based image analysis system. The deep learning model processes mammography images to generate objective breast tissue density classifications, eliminating the subjectivity and variability inherent in physician visual assessment while maintaining operational efficiency through automation.
2Adaptability or versatility
If multiple classification standards are used to accommodate changing guidelines, then the system remains adaptable, but the measurement precision deteriorates due to inconsistencies between standards
Solution Approach 1:
The deep learning model is trained to produce classifications that are consistent across different breast tissue density assessment standards (such as BIRADS categories). The model generates standardized output classifications that can be universally applied regardless of which specific guideline version is referenced, ensuring measurement precision while maintaining adaptability to different clinical contexts.
3Device complexity
If subjective classification criteria are used, then the device complexity is low, but the manufacturing precision of classification deteriorates due to high variability in assessment
Solution Approach 1:
The patent replaces simple subjective classification criteria with a sophisticated deep learning-based automated classification system. Although the computational model is complex, it delivers high classification precision by objectively analyzing image features and generating consistent density classifications, thereby improving manufacturing precision at the cost of increased device complexity.
Data Source
AI summary
Methods and systems and computer readable media are provided for processing digitized radiology images of a patient's tissue to perform a tissue density assessment. A set of radiology images is analyzed with a tissue density image classifier, wherein the tissue density classifier includes a plurality of classifiers. A features vector is generated, for each processed image in the set of radiology images, based on the processing. The features vector is provided, for each processed image in the set of radiology images, to a tissue density classifier to obtain an output. A final tissue density score is assigned based on the output of the tissue density classifier.