Hierarchical Lesion Detection via Uncertainty-Guided Resolution Switching
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Solution Overview
Problem
Current computer-aided diagnosis (CAD) systems for microscopy images are computationally expensive and time-consuming, especially when processing large high-resolution images, which can be redundant for confident low-magnitude diagnoses and limit accuracy by only analyzing at a fixed high magnitude.
Innovation Solution
A hierarchical image processing technique that performs initial analysis at a low resolution, generates an uncertainty map, identifies uncertain regions, and switches to higher resolution analysis only where necessary, fusing information from different scales for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution inspection is performed on all tissue images, then detailed structure and diagnosis accuracy are maintained, but computation time and processing cost increase significantly
Solution Approach 1:
The patent segments the tissue image processing into two distinct stages: a first stage processing the entire image at low resolution to identify uncertain regions, and a second stage processing only those uncertain regions at high resolution. This segmentation allows the system to maintain diagnostic accuracy for critical areas while avoiding unnecessary high-resolution processing of confident regions, thereby reducing overall computation time.
Solution Approach 2:
The patent applies local quality by differentiating the resolution quality applied to different regions of the image. Confident regions are processed at low resolution while uncertain regions are processed at high resolution. This selective application of quality levels ensures that computational resources are concentrated where they are most needed for accurate diagnosis, rather than uniformly applying high resolution across the entire image.
2Productivity
If low resolution analysis is used for all images, then computation speed increases, but diagnosis accuracy decreases for complex cases
Solution Approach 1:
The patent implements a dynamic processing strategy where the resolution level is adaptively adjusted based on the confidence score from the low-resolution analysis. Regions with high confidence remain at low resolution for speed, while regions with low confidence are dynamically upgraded to high resolution for accuracy. This dynamic adaptation allows the system to optimize the balance between computation speed and diagnosis accuracy for each specific case.
3Manufacturing precision
If fixed high magnitude analysis is applied to all regions, then detailed tissue structure is preserved, but processing time increases and real-time capability is lost
Solution Approach 1:
The patent applies partial action by performing high-resolution analysis only on the subset of regions that are identified as uncertain, rather than applying high-resolution analysis to the entire image. This partial application of excessive detail (high resolution) where needed, combined with low-resolution processing elsewhere, enables the system to maintain tissue structure detail for critical regions while achieving real-time processing capabilities.
Data Source
AI summary
A method and apparatus include performing a first image analysis at a first resolution of an input tissue image. An uncertainty map is generated based on performing the first image analysis. A set of uncertain regions of the input tissue image are identified based on the uncertainty map. A second image analysis of the set of uncertain regions is performed at a second resolution of the tissue image that is greater than the first resolution. An analysis result is generated based on the first image analysis and the second image analysis.