Magnification-Aligned Transformer for Fast Pathology Image Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep learning models for pathological image analysis face a trade-off between model performance and efficiency due to the high computational requirements of high-magnification images and significant information loss in low-magnification images, making them unsuitable for rapid clinical diagnosis.
Innovation Solution
A magnification-aligned Transformer (MAT) model that employs a self-supervised magnification alignment mechanism and a CNN-Transformer attention mechanism to align and integrate features across different magnifications, utilizing a global-local Transformer classification module to explore both global and local information in pathological images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-magnification images (400× or 200×) are used for pathological image analysis, then model performance is improved, but computational time and resource consumption increase significantly
Solution Approach 1:
The patent segments the high-magnification image processing task into multiple magnification levels (400×, 200×, 100×). The model processes images at different magnifications and fuses the results, allowing it to capture fine details at high magnification while using lower magnification for broader context, thereby reducing overall computational burden while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces a magnification dimension to the traditional single-magnification analysis approach. By processing images at multiple magnification levels simultaneously and fusing features across these dimensions, the model achieves both high precision (from 400×) and efficiency (from 100×) without being constrained to a single magnification level.
2Productivity
If low-magnification images (100× or 50×) are used to reduce computational cost, then processing speed is improved, but image information is severely lost
Solution Approach 1:
The patent merges information from multiple magnification levels by fusing features extracted at 400×, 200×, and 100×. This fusion combines the detailed information from high-magnification images with the broader contextual information from low-magnification images, recovering the information that would be lost if only low-magnification images were used.
Solution Approach 2:
The patent uses intermediate magnification levels (200×) as mediators between the highest (400×) and lowest (100×) magnification levels. These intermediate representations help bridge the information gap, allowing the model to progressively refine features from coarse to fine scales without directly processing only the lowest magnification images.
3Measurement precision
If high-magnification images are processed, then diagnostic accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent implements a dynamic multi-magnification processing strategy where the model adaptively processes images at different magnification levels based on their diagnostic value. Rather than statically processing all images at the highest magnification, the system dynamically selects and fuses features from multiple levels, reducing computation resources while maintaining diagnostic accuracy.
4Loss of time
If low-magnification images are used, then time and computation resource consumption are reduced, but model accuracy degrades
Solution Approach 1:
The patent segments the feature extraction process into multiple magnification-specific branches that operate in parallel. Each branch extracts features at a specific magnification level (400×, 200×, 100×), and these segmented features are subsequently fused. This segmentation allows the model to efficiently process low-magnification images while recovering accuracy through multi-scale feature fusion.
Data Source
AI summary
A method for analyzing pathological images based on a magnification-aligned transformer (MAT) is provided, in which a pathological image dataset is identified and segmented to obtain pathological image patches; the pathological image patches is screened to obtain a patch set; an MAT classification network model including a self-supervised magnification alignment module and a global-local Transformer classification module is constructed; the MAT classification network model is trained for self-supervised magnification alignment using the patch set in the self-supervised magnification alignment module; the MAT classification network model is further trained using a convolutional neural network (CNN)-transformer; and a pathological image classification prediction result is obtained using the trained MAT classification network model. A system for implementing such method is also provided.