Whole-Slide Image Superpatch Graphs for Interpretable Pathology Analysis
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Solution Overview
Problem
Conventional deep learning algorithms struggle to reflect the heterogeneity of cancer tissues and tumor microenvironments in high-resolution pathology images, limiting their interpretability and ability to identify significant patterns for patient prognosis and treatment success.
Innovation Solution
A method and apparatus utilizing graph deep learning to compress whole-slide images into superpatch graphs, employing graph neural networks (GNN) to embed node and context features, calculate contributions, and extract diagnostic information through environmental graph biomarkers, enabling the analysis of heterogeneous patterns in cancer tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep learning algorithms are used to analyze high-resolution pathology images, then predictive performance can be achieved, but the algorithms cannot reflect heterogeneity of cancer tissues and tumor microenvironment
Solution Approach 1:
The pathology image is divided into multiple patches, which are then organized into a graph structure where each patch becomes a node. This segmentation allows the model to capture local heterogeneous patterns while maintaining global context through graph connections, resolving the contradiction between achieving predictive performance and reflecting tissue heterogeneity.
Solution Approach 2:
The patent transitions from traditional 2D convolutional processing to 3D graph structure processing, adding a topological dimension to the analysis. This dimensional change enables the model to simultaneously capture spatial relationships and heterogeneous patterns across different scales, improving both predictive performance and adaptability to tissue variability.
2Measurement precision
If deep learning algorithms process entire high-resolution pathology images, then comprehensive pattern recognition is possible, but computational complexity and resource requirements increase significantly
Solution Approach 1:
By segmenting the large pathology image into smaller patches and representing them as graph nodes, the computational problem is divided into manageable units. This segmentation reduces memory requirements and allows parallel processing while maintaining the ability to recognize comprehensive patterns through graph neural network aggregation.
Solution Approach 2:
The graph structure serves as an intermediary representation between the raw high-resolution image and the final classification output. This intermediate graph representation compresses the large image data into a structured format that retains essential pathological patterns while reducing computational complexity for subsequent processing.
3Productivity
If conventional CNN models are used for pathology image analysis, then processing speed is maintained, but interpretability of learned patterns by medical professionals is limited
Solution Approach 1:
The graph neural network provides feedback mechanisms through attention weights and node importance scores that highlight which patches and features contribute most to the diagnosis. This feedback enables medical professionals to interpret the model's reasoning while maintaining efficient processing speeds through optimized graph operations.
Solution Approach 2:
The patent employs visualization techniques where different graph nodes and edges are colored or highlighted based on their importance or predicted class. This visual encoding transforms abstract computational results into interpretable graphical representations that medical professionals can easily understand while preserving processing efficiency.
Data Source
AI summary
The present invention relates to a method and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning, which may include: a whole-slide image (WSI) compression step of compressing WSI into a superpatch graph; a graph neural networks (GNN) analysis step of embedding node features and context features into the superpatch graph through a GNN model and calculating contributions for each node and edge; a biomarker acquisition step of classifying and grouping the superpatch graph according to the contributions for each node, connecting the classified and grouped superpatch graph in units of groups to generate connected graphs, normalizing and clustering features of the connected graphs, and acquiring environmental graph biomarkers for each group; and a diagnostic information extraction step of extracting and providing diagnostic information on the WSI based on the environmental graph biomarker for each group.