Digitized Image Partitioning for Batch-Effect Robustness
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Solution Overview
Problem
Batch effects in digitized images, such as those caused by differences in staining or equipment, lead to inconsistent performance of machine learning classifiers trained on datasets from multiple sites, reducing model robustness and accuracy.
Innovation Solution
A method and apparatus that utilize image characterization metrics to systematically partition digitized images into training and validation sets based on similar presentational properties, ensuring that each set includes images from different batch effect groups, thereby training the classifier to handle diverse data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If machine learning classifiers are trained on datasets from multiple sites, then the quantity and diversity of training data is improved, but batch effects from technical variations reduce model robustness and accuracy
Solution Approach 1:
The patent segments the training process by creating separate batch effect groups based on technical variations (staining protocols, equipment models, technicians). The classifier is trained to recognize and adapt to these segmented groups, allowing it to handle diversity while maintaining robustness within each segment.
Solution Approach 2:
The patent changes the parameter space by incorporating batch effect group identifiers and technical variation parameters into the training process. This allows the classifier to learn parameters that are invariant to technical variations while maintaining sensitivity to biological signals, thereby improving reliability across diverse datasets.
2Adaptability or versatility
If data is collected from different batches with different preparation methods and equipment, then the versatility and applicability of the model is improved, but technical variations introduce batch effects that reduce consistency
Solution Approach 1:
The patent applies preliminary action by pre-processing the data to identify and characterize batch effects before training the classifier. Batch effect groups are formed based on technical variations, and this preprocessing step ensures that the classifier is exposed to structured, labeled technical variations that it can learn to compensate for, thereby maintaining consistency across diverse batches.
Solution Approach 2:
The patent introduces batch effect group identifiers and technical parameters as intermediary variables that mediate between the raw diverse data and the classifier. These intermediaries allow the model to account for technical variations without being confounded by them, enabling versatility while maintaining stability through the mediating structure.
3Productivity
If random partitioning of digitized images is used for training and validation sets, then the simplicity and speed of data preparation is improved, but batch effects may cause the training and validation sets to have different presentational properties, reducing model performance
Solution Approach 1:
The patent applies preliminary action by pre-grouping images into batch effect groups based on technical characteristics before partitioning. This preliminary organization ensures that subsequent random partitioning maintains representation of all batch effect groups in both training and validation sets, preventing the mismatch problem while preserving the simplicity of random sampling.
Solution Approach 2:
The patent applies local quality by ensuring that each batch effect group is locally represented in both training and validation sets. This means that within each technical variation group, the partitioning maintains appropriate distribution, ensuring that the validation set accurately reflects the training set's characteristics for each specific batch condition, thereby improving measurement precision.
Data Source
AI summary
The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations. The operations include extracting one or more image characterization metrics from respective ones of a plurality of digitized images within an imaging data set. The plurality of digitized images have batch effects. The operations further include identifying a plurality of batch effect groups of the digitized images using the one or more image characterization metrics, and dividing the plurality of batch effect groups between a training set and/or a validation set. The training set and/or the validation set include some of the plurality of digitized images associated with respective ones of the plurality of batch effect groups.