3D Image Report Generation Using Multi-Module Machine Learning
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Solution Overview
Problem
Current methods for generating reports from 3D images are limited in providing comprehensive and accurate analysis, as they often rely solely on volume features without incorporating semantic representations, leading to incomplete or unclear reports that lack descriptive and uncertainty statements.
Innovation Solution
A method and apparatus utilizing multiple machine learning modules to identify volume features and semantic representations from 3D images, where a first module extracts volume features, a second module identifies semantic attributes, and a third module generates reports based on both, using pre-defined element sets and conditional probability calculations to ensure comprehensive and well-arranged content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If only volume features are used to generate reports, then the report generation process is simpler, but the report completeness and accuracy deteriorate
Solution Approach 1:
The system segments the report generation process into three distinct ML modules: a first module for volume feature identification, a second module for semantic representation identification, and a third module for report generation. This segmentation allows each module to specialize in specific tasks, improving overall report completeness while managing complexity through modular architecture.
Solution Approach 2:
The system merges volume features and semantic representations as dual input streams for the third ML module. By combining these two types of features, the system ensures that both structural information (volume features) and contextual information (semantic representations) are integrated into the final report, thereby improving completeness and accuracy.
2Measurement precision
If semantic representations are added to improve report accuracy, then the analysis precision improves, but the processing time increases
Solution Approach 1:
The first ML module performs preliminary extraction of volume features from the 3D image before the second module processes semantic representations. This preliminary action prepares the data in advance, allowing the subsequent modules to work more efficiently with pre-processed information, thereby reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The three ML modules operate in a continuous pipeline where the output of one module becomes the input of the next. This continuous processing flow eliminates idle time between stages and ensures that both volume features and semantic representations are processed seamlessly, improving analysis accuracy without excessive time loss.
3Reliability
If multiple ML modules are used to identify both volume features and semantic representations, then the report accuracy improves, but the device complexity increases
Solution Approach 1:
The system divides the complex task of report generation into three specialized ML modules, each responsible for a specific function: volume feature extraction, semantic representation extraction, and report synthesis. This segmentation improves reliability by ensuring each aspect is handled by dedicated processing units while managing complexity through clear functional separation.
Solution Approach 2:
The third ML module serves as a universal integrator that accepts both volume features and semantic representations as inputs and generates the final report. This multi-functional module consolidates the integration task, improving report accuracy through comprehensive information processing while avoiding the need for additional specialized modules.
4Ease of operation
If conditional probability calculations are performed for each semantic element, then the report organization improves, but the computational complexity increases
Solution Approach 1:
The third ML module dynamically selects semantic elements based on conditional probability calculations, adapting the report content to the specific characteristics of the input data. This dynamic approach improves report organization by ensuring that only relevant semantic elements are included, while the probabilistic framework manages computational complexity through efficient selection criteria.
Solution Approach 2:
The conditional probability mechanism provides feedback on the likelihood of each semantic element being relevant, allowing the system to iteratively refine the report content. This feedback loop improves report organization by prioritizing high-probability elements while managing computational resources through probability-based filtering of lower-priority elements.
Data Source
AI summary
Various techniques are provided for generating reports of three dimensional (3D) images. The techniques include identifying a plurality of volume features in a 3D image using a first machine learning (ML) module trained with annotated 3D images, and identifying a plurality of semantic representations associated with the 3D image using a second ML module trained with the annotated 3D images and reports associated with the annotated 3D images. The techniques further include generating a report of the 3D image based on the volume features and the semantic representations using a third ML module trained with the reports and outputs generated by the first ML module and the second ML module using the annotated 3D images and the reports.