Well Plate Cell Classification Using Z′ Loss for Effect Size
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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 features and metrics, which is time-consuming and prone to dead-ends, especially when dealing with novel treatments.
Innovation Solution
A classification model trained with a Z′ based cost function automatically classifies fluorescently labeled cells from well plate images, optimizing effect size by selecting relevant features that maximize the separation between control groups, reducing the need for manual annotation and iterative processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature selection and metric development is performed for effect size determination, then measurement precision can be achieved, but loss of time increases significantly
Solution Approach 1:
The system performs automatic feature selection and metric development without requiring manual biologist intervention. The machine learning model autonomously identifies relevant features and develops appropriate metrics for effect size determination, eliminating the time-consuming iterative process while maintaining measurement precision
Solution Approach 2:
The manual mechanical process of feature selection and metric development by biologists is replaced with an automated computational system. The machine learning model substitutes the human expert system, automatically performing feature extraction, selection, and metric optimization to determine effect size
2Measurement precision
If iterative process with multiple parameters is used for feature selection, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system automatically manages the complex multi-parameter feature selection process without requiring manual configuration or iteration. The machine learning model self-adjusts parameters and selects features based on the data, reducing the apparent complexity while maintaining measurement precision
3Productivity
If automated classification model is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The automated classification model replaces manual analysis processes, dramatically increasing productivity in effect size determination. Although the model itself is complex, it substitutes multiple manual steps and human expertise, resulting in net simplification of the overall workflow and faster results
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.