Histopathology Tile Labels from Slide-Level Weak Supervision
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Solution Overview
Problem
Training machine-learning models to identify regions of tissues or cells in histopathology images requires large datasets of manually annotated images, which is time-consuming, costly, and prone to human error.
Innovation Solution
Utilizing a slide-level class label preassigned by a histopathologist, the method extracts image tiles at different magnifications, inputs them into an ensemble of machine-learning models, and averages predictions to generate tile-level class labels, reducing the need for manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation of histopathology images is performed to train machine-learning models, then training data quality is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-assigning slide-level class labels to histopathology images before detailed tile-level annotation. This allows the machine-learning model to first learn from these preliminary labels, reducing the need for extensive manual tile-level annotation while maintaining training data quality.
Solution Approach 2:
The patent segments the histopathology image into multiple tiles at different magnifications, allowing the model to process and annotate each tile independently. This segmentation enables efficient use of slide-level labels to generate tile-level predictions without requiring manual annotation of every tile, thus reducing time consumption while maintaining quality.
2Measurement precision
If extensive manual annotation is performed to achieve accurate tile-level class labels, then prediction accuracy is improved, but cost and human error susceptibility increase
Solution Approach 1:
The patent implements self-service by enabling the machine-learning model to automatically generate tile-level class labels using slide-level labels as input. This self-service approach reduces dependency on manual annotation, lowering costs and minimizing human error while maintaining prediction accuracy through the model's learning capability.
Solution Approach 2:
The patent changes parameters by processing image tiles at multiple magnifications (e.g., 2x, 5x, 10x, 20x) rather than a single fixed resolution. This parameter variation allows the model to capture features at different scales, improving prediction accuracy while reducing the need for extensive manual annotation at any single magnification level.
3Ease of operation
If single-magnification image processing is used, then processing simplicity is maintained, but prediction accuracy across different resolutions deteriorates
Solution Approach 1:
The patent applies universality by designing the machine-learning model to process image tiles at multiple magnifications simultaneously. This multi-functional capability allows a single model to handle various resolutions and magnifications, improving prediction accuracy across different scales while maintaining operational simplicity through a unified processing framework.
Solution Approach 2:
The patent adds the dimension of magnification variation by processing image tiles at multiple magnification levels (2x, 5x, 10x, 20x) rather than a single fixed magnification. This dimensional expansion allows the model to capture features at different scales, significantly improving prediction accuracy while the automated multi-scale processing maintains operational simplicity.
Data Source
AI summary
A method implemented by one or more computer devices includes providing weakly-supervised neural networks for analysis of histopathology images. The method includes accessing a histopathology image including a slide-level class label. The method includes extracting a plurality of regions of pixels of the histopathology image at a plurality of magnifications. For each of the extracted plurality of regions of pixels, the method further includes inputting the region of pixels into a machine-learning model trained to generate a prediction of a class label for the region of pixels based on the region of pixels and the slide-level class label and outputting the prediction of the class label for the region of pixels. The method includes generating a prediction of one or more tile-level class labels for the histopathology image based on the predictions of class labels for each of the extracted plurality of regions of pixels.