AI Particle Classification via Proportion-Based Training
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Solution Overview
Problem
The challenge in microbiology is the tedious process of obtaining annotated training images for supervised learning neural networks, which requires manual annotation of biological particles, making it time-consuming and labor-intensive.
Innovation Solution
A method that uses an artificial intelligence algorithm trained by proportions of labels, where particles are annotated based on their proportions within a class, reducing the need for individual annotations and allowing for adaptive classification as new particles are acquired.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual individual annotation of particles is performed to train supervised learning neural networks, then the neural network can be properly trained to identify and classify particles, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system uses automated algorithms to perform annotation based on proportion labels rather than requiring manual individual particle annotation. The neural network learns from proportion-based annotations of entire images, allowing the system to self-train without extensive human intervention for each particle
Solution Approach 2:
The annotation approach changes from individual particle-level labels to image-level proportion labels. Instead of annotating each particle individually, the system uses proportion annotations (e.g., 70% positive, 30% negative) for entire images, fundamentally changing the parameter structure of training data
2Measurement precision
If exhaustive individual annotations are obtained for all particles, then the neural network achieves high classification accuracy, but the complexity and cost of data preparation increases significantly
Solution Approach 1:
The method extracts only the essential information needed for training by using proportion labels at the image level rather than requiring detailed individual particle annotations. This extraction approach captures the necessary training signal while avoiding the complexity of exhaustive annotation
Solution Approach 2:
Instead of performing complete individual annotation for every particle, the system uses partial annotation at the image level with proportion labels. This partial action approach provides sufficient training information without the excessive effort of full individual particle annotation
Data Source
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AI summary
A method for characterizing biological particles from a sample, the characterization aimed at determining a property of each particle, the method comprising: - a) measuring a characteristic of at least one particle of the sample; - b) processing the characteristic of the particle or particles by an artificial intelligence algorithm; - c) from the processing, characterizing the particles of the sample, so as to assign a class to each particle, each class being representative of the property of the particle; - the method being characterized in that the artificial intelligence algorithm has previously been trained by labels, from training samples, to each training sample being assigned a proportion of cells in each class, so that the learning is carried out according to the proportions respectively assigned to each training sample.