Hierarchical X-Ray Analysis for Accurate Region Highlighting
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Solution Overview
Problem
Conventional AI-based X-ray analysis mechanisms face issues such as incorrect highlighting of regions and inaccurate report generation, often being expensive and having complex algorithm structures, necessitating a cost-effective and sensitive system for anomaly detection and classification in digital diagnostic images.
Innovation Solution
A deep learning-based model is employed to analyze X-ray images, using hierarchical classification to identify relevant regions and determine abnormalities, generating clinically and grammatically accurate reports through a combination of deep learning and NLP models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CNN models are used for X-ray analysis, then abnormality detection capability is provided, but region highlighting accuracy deteriorates and report accuracy deteriorates
Solution Approach 1:
The patent divides the X-ray image analysis into multiple hierarchical levels: first identifying global abnormalities, then segmenting the image into multiple regions, and finally localizing abnormalities within specific regions. This multi-level segmentation approach improves both detection capability and localization accuracy compared to conventional single-stage CNN models.
Solution Approach 2:
The patent introduces a hierarchical dimension to the analysis process, moving from global image-level detection to region-level detection to specific abnormality localization. This dimensional progression allows the system to maintain high detection sensitivity while improving region highlighting accuracy through progressive refinement.
2Reliability
If conventional CNN models are used for X-ray analysis, then abnormality detection capability is provided, but report accuracy deteriorates
Solution Approach 1:
The report generation process is segmented into multiple stages: abnormality detection at the image level, region identification, and detailed abnormality characterization within each region. This segmentation allows for more accurate and structured report generation by systematically processing different aspects of the analysis separately and combining results.
Solution Approach 2:
The patent introduces an intermediary hierarchical structure between image input and final report output. This intermediary layer processes information through multiple levels of abstraction, allowing the system to generate more accurate reports by systematically organizing detection results, region characteristics, and abnormality details before final report compilation.
3Reliability
If conventional AI-based mechanisms are used, then X-ray analysis is performed, but cost increases and algorithm complexity increases
Solution Approach 1:
The complex analysis task is segmented into modular hierarchical levels, where each level handles specific aspects of the analysis. This modular segmentation simplifies the overall algorithm structure by breaking down complex operations into manageable, independent modules that can be processed sequentially, reducing overall system complexity while maintaining comprehensive analysis capability.
4Reliability
If comprehensive region analysis is performed, then detection sensitivity is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary global abnormality detection before conducting detailed region-level analysis. This preliminary action identifies which regions require detailed examination, allowing the system to focus processing resources on relevant areas only. This approach maintains high detection sensitivity while reducing overall processing time by avoiding exhaustive analysis of all regions regardless of abnormality presence.
Data Source
AI summary
A method of analyzing images and generating a report is disclosed. The method may include inputting a test image to a trained prediction model. The trained prediction model may be a deep learning-based model. The method may further include obtaining at least one abnormality associated with the test image, using the trained prediction model. The method may further include identifying at least one relevant predetermined region from a plurality of predetermined regions associated with the test image, using the trained prediction model, based on the at least one abnormality. Each of the at least one relevant predetermined region is identified based on hierarchical classification. The method may further include determining within each of the at least one relevant predetermined region, a location associated with the at least one abnormality.