Self-Supervised Tile Pairing for Histopathology Image Analysis
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Solution Overview
Problem
The scarcity of annotated training data in digital pathology hinders the development of effective machine learning models for tissue classification and disease prediction, due to the time-consuming and expensive process of manual annotation, and the subjectivity of label assignment by domain experts, leading to inconsistent data sets and reduced prediction accuracy.
Innovation Solution
A self-supervised learning method that splits digital images into tiles, automatically generates tile pairs with similarity labels based on spatial proximity, and trains a machine learning module using these labeled pairs to perform image analysis, eliminating the need for manual annotation and improving data efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation by domain experts is used to create training data, then the quality and accuracy of labels can be improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system performs self-annotation by automatically generating similarity labels based on spatial proximity calculations. The machine learning module trains on data that is automatically labeled without requiring domain expert intervention, thus eliminating the time-consuming manual annotation process while still producing usable training data for model development
Solution Approach 2:
The patent applies preliminary automated labeling using spatial proximity metrics before manual review. By pre-generating similarity labels based on distance calculations, the system prepares training data in advance, reducing the subsequent need for time-consuming manual annotation while maintaining a baseline quality that can be further refined
2Stability of the object's composition
If multiple domain experts annotate the same training data to reduce subjectivity, then the consistency of labels can be improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system uses automated spatial proximity calculations to generate consistent similarity labels without human intervention. This self-labeling process eliminates the variability and subjectivity inherent in manual annotation by multiple experts, producing uniform labels based on objective distance metrics that are inherently consistent across all data points
Solution Approach 2:
The patent transforms the annotation process from subjective human judgment to objective parameter-based labeling. By using spatial proximity (distance) as the labeling parameter, the system converts a qualitative, subjective task into a quantitative, objective measurement that naturally ensures consistency across all annotations without requiring multiple reviewers
3Measurement precision
If large annotated training data sets are created to improve model accuracy, then the prediction accuracy of machine learning models can be improved, but the annotation cost and time consumption increase significantly
Solution Approach 1:
The system automatically generates large volumes of training data with similarity labels through self-supervised learning. By utilizing the inherent spatial structure of the data and automatically computing proximity-based labels, the system can scale to create extensive training datasets without the linear increase in annotation time that would result from manual processes
Solution Approach 2:
The spatial proximity labeling approach is universally applicable to any dataset with spatial coordinates or positional information. This multi-functional labeling method can be applied across different domains and data types, enabling the creation of diverse large-scale training datasets using a single automated approach rather than domain-specific manual annotation for each dataset
4Ease of operation
If traditional handcrafted features are used for image analysis, then the interpretability of features can be improved, but the ability to capture complex tissue patterns is limited
Solution Approach 1:
The patent replaces traditional handcrafted feature engineering with automated spatial proximity calculations. Instead of manually designing features based on domain knowledge, the system uses algorithmic distance computations that automatically capture spatial relationships, thereby substituting manual feature design with a more versatile computational approach that adapts to complex patterns while maintaining interpretability through the clear spatial metric
Data Source
AI summary
The method includes receiving a plurality of digital images each depicting a tissue sample; splitting each of the received images into a plurality of tiles; automatically generating tile pairs, each tile pair having assigned a label being indicative of the degree of similarity of two tissue patterns depicted in the two tiles of the pair, wherein the degree of similarity is computed as a function of the spatial proximity of the two tiles in the pair, wherein the distance positively correlates with dissimilarity; and training a machine learning module—MLM—using the labeled tile pairs as training data to generate a trained MLM, the trained MLM being configured for performing an image analysis of digital histopathology images.