X-Ray Report Learning Using CT-Projected Pseudo Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interpretation of simple X-ray images is challenging due to their two-dimensional representation of three-dimensional organs, leading to low accuracy in interpretation reports, even with machine learning models trained on such data.
Innovation Solution
A learning apparatus and method that utilizes a processor to generate pseudo simple X-ray images from X-ray CT images with three-dimensional information, converting interpretation reports to enhance training, and adjusts the model using errors to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple X-ray images with two dimensional information are used for training, then the training process is simpler and faster, but the accuracy of interpretation reports decreases due to overlapping organs and loss of three dimensional shape information
Solution Approach 1:
The patent transforms three-dimensional CT images into two-dimensional pseudo-simple X-ray images through projection. This allows the model to learn from high-quality 3D data while outputting results in the 2D format required for clinical practice, resolving the contradiction between using simple 2D training data and achieving high accuracy
Solution Approach 2:
The patent introduces pseudo-simple X-ray images as an intermediary representation. These images are generated by projecting 3D CT images and serve as a bridge between the detailed 3D training data and the 2D simple X-ray images used in clinical practice, enabling the model to learn from high-quality 3D data while maintaining compatibility with 2D input requirements
2Measurement precision
If three dimensional X-ray CT images are used for training, then the accuracy of interpretation reports improves due to better organ shape representation, but the training process becomes more complex and computationally intensive
Solution Approach 1:
The patent projects three-dimensional CT images onto two-dimensional planes to create pseudo-simple X-ray images. This dimensionality reduction simplifies the training process and computational requirements while preserving the essential anatomical information needed for accurate interpretation reports
Solution Approach 2:
The patent creates pseudo-simple X-ray images as simplified copies of the original 3D CT images. These copies retain the critical anatomical structures and relationships needed for training while being computationally easier to process, thus reducing training complexity while maintaining accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach enables the generation of high-accuracy interpretation reports by leveraging three-dimensional information from X-ray CT images, improving the accuracy of simple X-ray image interpretation.
Implementation Method 1
a processor performs processing of inputting a pseudo simple X-ray image, which is generated by projecting the X-ray CT image
Data Source
AI summary
The learning apparatus includes a processor (129), a memory (114), and a learning model (126). The processor (129) performs processing of inputting a pseudo simple X-ray image (204), which is generated by projecting an X-ray CT image (202), to the learning model (126), processing of generating a second interpretation report (208) with respect to the pseudo simple X-ray image (204) by converting a first interpretation report (206), processing of acquiring an error between an estimation report (210) with respect to the pseudo simple X-ray image (204) output by the learning model (126) on the basis of the input pseudo simple X-ray image (204), and the second interpretation report (208), and processing of training the learning model (126) by using the error.