Microscope Workflow Automation Using Trained Models
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Solution Overview
Problem
Existing microscope systems face inefficiencies in data processing, limited application support, and high costs due to preprogrammed schemes, loss of original image information during postprocessing, and unpredictable malfunctions, leading to increased costs and time for experiments.
Innovation Solution
Implementing a method and apparatus that utilize trained models, such as neural networks, to capture data and make decisions in real-time, allowing for adaptive workflows, improved accuracy, and continuous model updates through data aggregation and feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If preprogrammed schemes are used to control microscope experiments, then the workflow can be automated, but the system cannot adapt to unexpected biological phenomena and requires complete termination to intervene
Solution Approach 1:
The patent implements feedback mechanisms where trained models continuously analyze recorded images and provide real-time feedback to the control unit. This enables the system to automatically adapt the workflow based on detected biological phenomena without requiring complete termination or manual intervention, resolving the contradiction between automation and adaptability.
Solution Approach 2:
The system dynamically adjusts the experimental workflow based on real-time analysis by trained models. The control parameters and acquisition conditions are continuously optimized according to the detected state of the specimen, transforming the static preprogrammed scheme into a dynamic adaptive process that maintains both automation and versatility.
2Reliability
If preprogrammed schemes record comprehensive data, then all possible biological phenomena can be captured, but excessive non-informative data increases storage and evaluation costs
Solution Approach 1:
Instead of recording all possible data comprehensively, the system uses trained models to perform partial action by selectively recording only the most relevant and informative data. The models predict which data points are likely to contain meaningful biological phenomena, allowing the system to capture sufficient information for reliable analysis while significantly reducing storage and evaluation costs.
Solution Approach 2:
The patent replaces the mechanical approach of comprehensive data recording with an intelligent system based on trained models. These models substitute the brute-force method of capturing all data with a selective, prediction-based approach that identifies and records only the most valuable information, reducing resource consumption while maintaining reliability.
3Adaptability or versatility
If conventional systems purchase additional upgrades for new applications, then the spectrum of application can be expanded, but high costs are incurred
Solution Approach 1:
The patent implements a universal platform where a single trained model system can handle multiple different applications and specimen types. By training models on diverse datasets and using transfer learning, the system achieves multi-functionality without requiring separate hardware upgrades for each new application, significantly reducing costs while expanding the spectrum of usable applications.
Solution Approach 2:
The system expands its application spectrum by changing parameters in the trained models rather than through hardware modifications. Different applications are handled by adjusting model parameters, training data, and processing conditions, allowing the same physical system to adapt to new applications cost-effectively through software-based parameter changes.
4Measurement precision
If feedback methods are used to capture image recording conditions, then the accuracy can be improved, but reliable feedback is only possible after a sufficiently large number of recorded images
Solution Approach 1:
The patent applies preliminary action by pre-training models on large datasets before actual experimentation. This preliminary training phase allows the models to learn from extensive data in advance, so that during actual experiments, reliable feedback can be provided much faster without requiring accumulation of large numbers of images during the measurement process itself.
Solution Approach 2:
The system provides beforehand cushioning by using pre-trained models that have already learned from extensive training data. This cushioning effect allows the system to maintain high measurement precision from the beginning of experiments without needing to accumulate sufficient data during the actual measurement, as the models are already prepared with prior knowledge.
5Measurement precision
If postprocessing of images is used to optimize results, then the accuracy can be improved, but information from the recorded original images may be lost
Solution Approach 1:
The patent inverts the conventional approach by applying trained models to the original recorded images before any postprocessing losses occur. Instead of recording images and then losing information during postprocessing, the system uses AI models to extract and preserve critical information directly from the original images, maintaining measurement precision while preventing information loss by reversing the traditional workflow sequence.
Data Source
AI summary
An apparatus for optimizing workflows of one or more microscopes and/or microscope systems includes one or more processors and one or more computer-readable storage media. The one or more computer-readable storage media have stored therein computer-executable instructions, which, when executed by the one or more processors cause execution of the following steps: implementing, by one or more components of the one or more microscopes and/or microscope systems, a workflow comprising a capture of first data; applying one or more trained models to the captured first data; and making at least one decision in relation to the workflow based on the application of the one or more trained models to the captured first data.