Classifier Training for Molecular Biological Examinations
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Solution Overview
Problem
Automated analytical systems, such as lab-on-a-chip systems for medical testing, face challenges in achieving high sensitivity and specificity, particularly in detecting pathogens in biological samples, which affects the accuracy of test results.
Innovation Solution
A method for training a classifier that involves subdividing input signals from evaluation points into multiple signals, using neural networks to ascertain representations, and adapting parameters based on loss values to improve classification accuracy, allowing for precise detection of pathogens in biological samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is used for medical testing of biological samples, then the testing accuracy can be improved, but the demands on sensitivity and specificity become extremely high
Solution Approach 1:
The input signal is subdivided into multiple partial input signals corresponding to different evaluation points in the molecular biological examination system. Each partial input signal is processed independently to generate a representation, which are then combined to form the final classification. This segmentation allows the classifier to focus on specific local features that are critical for accurate pathogen detection.
Solution Approach 2:
Different evaluation points in the molecular biological examination are treated with different processing approaches. Each evaluation point's partial input signal is processed to extract locally relevant features, allowing the system to adapt to the specific characteristics of different measurement locations and improve overall classification accuracy.
2Device complexity
If the classifier processes the entire input signal as a whole, then the processing is simpler, but the classification accuracy for medical test results decreases
Solution Approach 1:
The input signal is divided into multiple partial input signals corresponding to different evaluation points. Each partial input signal is processed independently through the classifier to generate a representation. This segmentation enables the system to capture local patterns and features that would be lost in holistic processing, thereby improving classification accuracy for medical test results.
Solution Approach 2:
The approach transforms the processing from a single holistic input to multiple parallel processing streams. By treating each evaluation point as a separate dimension of analysis, the system can extract features from multiple perspectives and combine them to achieve more accurate classification than processing the entire signal as a single unit.
Data Source
AI summary
A computer-implemented method for training a classifier. The method includes: ascertaining a first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and a desired output signal characterizing a classification of the evaluation points is allocated to the first input signal; subdividing the first input signal into a plurality of second input signals according to an arrangement of the evaluation points; ascertaining a plurality of first representations, a first representation being ascertained for each second input signal of a first subset of the plurality of second input signals using the classifier; ascertaining an output signal using the classifier and based on the plurality of first representations, the output signal characterizing a classification of the first input signal; adapting at least one parameter of the classifier according to a loss value which characterizes a difference between the ascertained output signal and the desired output signal.