Cell Analysis AI Algorithm Selection for Multi-Item Efficiency
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Solution Overview
Problem
As the number of analysis items increases in cell analysis, existing methods require more extensive training of machine learning models, leading to increased training time and model complexity, making efficient analysis of multiple items challenging.
Innovation Solution
A cell analysis method that selects an appropriate artificial intelligence algorithm from a plurality of algorithms based on the analysis items, generating data indicating cell properties by inputting analysis data to the selected algorithm, facilitating efficient analysis of multiple items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of analysis items increases, then the comprehensiveness of cell analysis is improved, but the training time and model complexity increase
Solution Approach 1:
The patent divides the analysis process into two independent segments: a shared feature extraction phase that processes all analysis items, and separate determination phases for each specific analysis item. This segmentation allows the system to handle multiple analysis items without proportionally increasing training time, as the feature extraction portion is trained once and reused across all items.
Solution Approach 2:
The patent creates a universal feature extraction model that serves multiple analysis items simultaneously. This multi-functional approach allows the same extracted features to be used for determining various cell properties (such as presence/absence, concentration, and morphology), eliminating the need to train separate models for each analysis item and thereby reducing overall training time while maintaining comprehensive analysis capability.
2Adaptability or versatility
If the number of analysis items increases, then the comprehensiveness of cell analysis is improved, but the model complexity increases
Solution Approach 1:
The patent segments the machine learning model into a shared feature extraction component and multiple simple determination components. This segmentation reduces model complexity by avoiding the need for large, complex models for each analysis item, instead using a single moderate-complexity feature extraction model that feeds into multiple simpler determination models.
Solution Approach 2:
The patent implements a universal feature extraction model that performs multiple functions by extracting features applicable to all analysis items. This universal approach reduces overall model complexity compared to having separate specialized models for each analysis item, as the shared features can be reused across different determination tasks.
3Measurement precision
If more trainings are conducted to reduce determination errors, then the accuracy is improved, but the training time increases
Solution Approach 1:
The patent performs preliminary feature extraction training once, before conducting determination training for each analysis item. This preliminary action creates a reusable foundation that reduces the overall training time required to achieve high accuracy across multiple analysis items, as the feature extraction portion does not need to be retrained for each determination task.
Data Source
AI summary
The present invention is to facilitate analysis of a plurality of analysis items. A cell analysis method for analyzing cells is provided in which data for analysis of cells contained in a sample are generated, and an artificial intelligence algorithm to be the input destination of the generated analysis data is selected from among a plurality of artificial intelligence algorithms, data indicating the properties of the cells are generated based on the analysis data via the artificial intelligence algorithm.