Microscopic Analysis Using Machine Learning Transfer Information
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional transmitted light microscopy techniques provide limited information content about cell samples, requiring higher technical effort and complexity compared to other microscopy methods.
Innovation Solution
An optical detection system utilizing machine learning and neural networks for evaluating uncolored cell samples, employing different illumination parameters such as polarization and color coding to generate multiple detection information sets, which are then processed to enhance information content without relying on physical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transmitted light microscopy is used for sample analysis, then the technical structure remains simple and cost-effective, but the information content obtained from the sample is limited
Solution Approach 1:
The detection information is divided into multiple parameter spaces (e.g., polarization, color, focus) that are processed independently through separate neural network branches. Each branch extracts specific features from different parameter dimensions, and the results are combined to achieve comprehensive sample analysis with enhanced information content while maintaining a modular system structure
Solution Approach 2:
The invention extends the analysis from single-dimensional intensity information to multi-dimensional parameter spaces including polarization, color, and focus dimensions. By capturing and processing information across multiple dimensions simultaneously, the system extracts significantly more sample information without proportionally increasing hardware complexity
2Measurement precision
If multiple detection parameters are used to increase information content, then the analysis accuracy improves, but the technical effort and system complexity increase
Solution Approach 1:
The system employs self-service through automated neural network-based evaluation that processes multiple detection parameters without requiring manual intervention. The trained models automatically extract features, classify sample characteristics, and generate analysis results, reducing the technical effort and expertise required while maintaining high analysis accuracy across multiple parameters
Solution Approach 2:
The neural networks are pre-trained on large datasets of detection information with known outcomes. This preliminary training enables the system to automatically recognize patterns and make accurate predictions without requiring complex real-time processing or expert intervention during actual sample analysis, thereby reducing technical effort while maintaining high precision
3Extent of automation
If conventional microscopy methods are used, then the system structure remains simple, but the ability to extract relevant sample information without manual intervention is limited
Solution Approach 1:
The invention replaces manual evaluation mechanisms with automated neural network-based systems. Instead of requiring expert operators to visually analyze and interpret microscopy images, trained deep learning models automatically process detection information across multiple parameters, extract relevant features, and generate diagnostic results, thereby significantly increasing automation while the modular architecture keeps system complexity manageable
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach increases the information content of sample detection, reduces technical complexity, and allows for reliable analysis of cell samples with high accuracy, independent of the optical detection system's properties.
Implementation Method 1
performing an evaluation of the detection information, in particular by an analysis means (in particular based on machine learning), preferably by means of transfer information taught by machine and/or by a neural network
Implementation Method 2
The transfer information may include, for example, a classifier or a model or the like. The preferred feature of transfer information is that it was generated automatically by artificial intelligence (i.e. in particular in the learning method of a neural network)
Implementation Method 3
The detection information can differ from each other at least with regard to one illumination parameter of the detection system (e.g. coded, in particular polarization and/or color coded)
Implementation Method 4
The detection information can differ from each other at least with regard to one illumination parameter of the detection system (e.g. coded, in particular polarization and/or color coded)
Data Source
AI summary
The invention relates to a method for microscopic evaluation (120) of a sample (2), in particular at least one uncolored object or cell sample (2), in an optical detection system (1), where the following steps are performed:providing at least two different detection information (110) about the sample (2), in particular by the detection system (1),performing an evaluation (120) of the detection information (110), in particular by an analysis means (60), on the basis of machine-learned transfer information (200), in order to determine result information (140) about the sample (2),the transfer information (200) being trained for a different detection parameterization of the detection information (110), in which the detection information (110) differs from one another in terms of at least one illumination parameter of the detection system (1), in particular in terms of polarization and/or color coding.