Cell Classification Model for Effect Size Optimization in Bioassays
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Solution Overview
Problem
Fluorescence microscopy-based bioassays face challenges in efficiently determining effect size due to complex biochemistry and the iterative process of selecting relevant features and metrics, leading to time-consuming analysis, especially for novel treatments.
Innovation Solution
A classification model trained with a Z′ based cost function automatically classifies fluorescently labeled cells from well plate images, using a convolutional neural network to optimize effect size by selecting features that maximize class separation, reducing the need for manual annotation and iterative processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature selection and metric development are used for effect size determination, then the analysis can be tailored to specific biochemical contexts, but the process becomes extremely time-consuming and iterative
Solution Approach 1:
The classification model automatically performs feature selection and effect size optimization without requiring manual intervention. The system self-adjusts by training on control well images and autonomously identifies relevant features, eliminating the need for researchers to manually develop protocols and iterate through multiple analysis configurations.
Solution Approach 2:
The patent replaces the manual mechanical process of feature selection and metric development with an automated machine learning system. The classification model uses computational algorithms to automatically determine effect size metrics, substituting human analysts' manual work with automated image processing and statistical analysis.
2Productivity
If traditional effect size metrics are used with pre-known features, then the measurement can be straightforward, but the approach fails for novel treatments with unknown features
Solution Approach 1:
The system performs preliminary learning by training the classification model on control well images before analyzing test samples. This preliminary training phase enables the model to automatically identify relevant features and establish baseline characteristics, allowing rapid analysis of novel treatments without requiring pre-specified features or manual protocol development.
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on the specific treatment being tested. The classification model automatically modifies feature selection and metric parameters according to the characteristics of each treatment group, enabling flexible adaptation to novel treatments while maintaining consistent automated analysis speed.
3Reliability
If extensive manual protocol development is performed to optimize features and metrics, then the analysis can be highly customized, but the iterative process creates many dead-ends and local minima
Solution Approach 1:
The classification model uses feedback from control well analysis to continuously optimize its feature selection and classification parameters. During training, the model receives feedback on classification accuracy and automatically adjusts its parameters to improve performance, eliminating the need for manual protocol iteration and reducing analysis dead-ends through automated parameter optimization.
Data Source
AI summary
Methods and systems are provided herein for automatic object classification. In an example, a method includes receiving one or more images of a plate including a plurality of wells, each well including a plurality of cells, the plurality of wells including a first control well, a second control well, and at least one test well, classifying one or more cells from the at least one test well using a trained classification model, the trained classification model trained based on training data including instance images of the first control well and the second control well formed from the one or more images of the plate and further based on an effect size loss function, and outputting an indication of the classified one or more cells.