Whole Slide MSI Prediction Using Self-Attention Patch Weighting
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Solution Overview
Problem
Existing methods for predicting microsatellite instability (MSI) from whole slide images using convolutional neural networks (CNNs) fail to accurately account for the varying influence of different tissue patches, leading to reduced accuracy in MSI prediction due to uniform weighting of features across patches.
Innovation Solution
A method utilizing a self-attention mechanism to encode tumor patches as feature vectors, determine their weights, and compute a weighted sum to predict MSI scores, incorporating the influence of each patch in the whole slide image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all patches are given the same weight and averaged directly, then the processing is simple and fast, but the accuracy of MSI prediction deteriorates because significant differences between patches are ignored
Solution Approach 1:
The patent applies local quality by assigning different weights to different patches based on their individual characteristics and importance. The self-attention mechanism calculates unique weights for each patch, allowing the system to focus more on significant regions while reducing the influence of less important areas, thereby improving prediction accuracy without sacrificing too much processing efficiency.
Solution Approach 2:
The patent changes the parameter of patch weighting from uniform (all patches having the same weight) to variable (each patch having a dynamically calculated weight based on its features). This parameter change enables the system to adaptively emphasize important patches and de-emphasize less important ones, resolving the contradiction between simple processing and accurate prediction.
2Device complexity
If only one in thousands of features from the feature map is obtained, then the computational complexity is reduced, but the reliability of MSI prediction deteriorates due to insufficient feature representation
Solution Approach 1:
The patent extracts multiple important features from the feature map rather than relying on a single feature. The self-attention mechanism identifies and extracts relevant features from different patches, combining them with appropriate weights to form a comprehensive representation that maintains reliability while managing computational complexity.
Solution Approach 2:
The patent merges features from multiple patches into a unified representation through weighted summation. Instead of using only one feature, the system combines features from all patches with different weights, preserving important information while maintaining computational feasibility through the aggregated feature vector.
3Ease of operation
If patches with significant influences and patches with no influences are treated equally, then the processing is uniform and simple, but the manufacturing precision of MSI scoring deteriorates
Solution Approach 1:
The patent applies local quality by treating different patches differently based on their characteristics. The self-attention mechanism assigns higher weights to patches with significant influences on MSI prediction and lower weights to patches with minimal influence, thereby improving scoring precision while maintaining a relatively simple processing framework.
Solution Approach 2:
The patent introduces dynamics into the processing by making patch weights adaptive rather than static. The weights are dynamically calculated based on the content and importance of each patch, allowing the system to automatically adjust the treatment of different patches according to their actual significance, thus improving precision without complex manual intervention.
Data Source
AI summary
A method for predicting a microsatellite instability (MSI) from a whole slide image based on a self-attention mechanism, in which an image of a tissue region obtained through cellular staining on a tissue sample of a patient serves as a target whole slide image to be predicted. The tissue region is segmented into tissue patches, and stain normalization is performed with staintools. N tumor patches are screened out from the tissue patches, and the N tumor patches are inputted to a preset feature extraction model. Each of the tumor patches are encoded as an M-dimensional feature vector, thereby obtaining a N×M feature matrix; and the N×M feature matrix is inputted to a pre-trained prediction model, such that the prediction model outputs an MSI score of the target whole slide image. The present disclosure predicts the MSI based on deep learning and the cellular staining.