Cell Density Grouping with Self-Encoder and Twin Networks
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Solution Overview
Problem
Existing image processing methods are inefficient in calculating the number and volume of cells due to the complexity of grouping different cell densities, which prolongs the calculation process.
Innovation Solution
A method utilizing a self-encoder and twin network model to group cell densities by inputting images into a preset number of density grouping models, calculating error values, and determining the minimum error to define the density range, with training processes to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image processing methods are used to calculate cell number and volume, then the calculation can be performed, but the process becomes complex and time-consuming
Solution Approach 1:
The patent segments the cell density grouping task by dividing cells into multiple density groups (e.g., low density, medium density, high density) based on their spatial distribution characteristics. This segmentation allows the complex grouping process to be broken down into simpler, manageable steps that can be processed more efficiently by the neural network.
Solution Approach 2:
The patent replaces traditional mechanical image processing methods with a neural network-based system. The neural network automatically learns to identify and group cells by density through training, eliminating the need for complex manual algorithms and significantly improving calculation efficiency while reducing process complexity.
2Measurement precision
If different densities of cells are grouped manually or using traditional algorithms, then cell density classification is achieved, but the calculation time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on labeled cell density data before actual analysis. This training phase allows the network to learn optimal density grouping patterns in advance, so that during actual cell analysis, the network can quickly and accurately classify cell densities without requiring time-consuming real-time calculations.
Solution Approach 2:
The patent uses the concept of copying by creating a neural network model that replicates the complex density grouping task. The network is trained on representative samples and then used to infer cell density groups in new images, effectively copying the analytical capability without requiring repeated complex calculations for each new image.
3Manufacturing precision
If complex algorithms are used to group cell densities, then more accurate density ranges can be determined, but the processing speed decreases
Solution Approach 1:
The patent replaces traditional computational algorithms with a neural network system that achieves high accuracy through learned patterns rather than complex calculations. The network processes cell density information through parallel neural computations, maintaining manufacturing precision while significantly increasing processing speed compared to sequential algorithmic approaches.
Data Source
AI summary
A method of grouping certain cell densities to establish the number and volume of cells appearing in an image input the image into a self-encoder having a preset number of a density grouping models to obtain a preset number of reconstructed images. The image and each reconstructed image are input into a twin network model of the density grouping model corresponding to each reconstructed image, and a first error value is calculated between the image and each reconstructed image. A minimum first error value in the first error value set is determined, and a density range corresponding to the density grouping model corresponding to minimum first error value is taken as the density range. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.