An Adaptive Feature Extraction Method and System for Quantitative Detection of Fluorescent Immunochromatograms

By constructing a three-dimensional interferometric perturbation tensor and sparse principal component analysis, combined with self-supervised triplet training samples and weighted spectral entropy, the problem of inaccurate identification of response regions in existing quantitative detection of fluorescence immunoassay layers is solved, and high-precision quantitative detection of fluorescence immunoassay layers is achieved.

CN120877013BActive Publication Date: 2026-01-30HUNAN AIFANG BIOTECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510999246.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-21
Publication Date
2026-01-30
Estimated Expiration
2045-07-21

AI Technical Summary

Technical Problem

Existing quantitative detection methods for fluorescence immunoassay lack in-depth modeling of the dynamic changes in the immune response process and cannot capture subtle perturbation features in image sequences. This results in insufficient accuracy in response region identification and robustness in quantitative output, making it difficult to meet the requirements for high-precision detection.

Method used

By acquiring fluorescent speckle image sequences, calculating the image gradient difference between adjacent frames, constructing a three-dimensional interferometric perturbation tensor, and obtaining the principal perturbation mode map through sparse principal component analysis, training an image block encoder based on self-supervised triplet training samples, generating a response probability map, using weighted spectral entropy values ​​for map fusion, setting a mask screening threshold to obtain the immune response region, and calculating the quantitative detection concentration value through concentration fitting.

Benefits of technology

It enhances the ability to identify early local response regions, improves the spatial recognition accuracy of response regions, suppresses local noise interference, and improves the stability and robustness of response spectra, thus meeting the requirements of high-precision quantitative detection.

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Abstract

This invention discloses an adaptive feature extraction method and system for quantitative fluorescence immunoassay, relating to the field of fluorescence immunoassay technology. The method includes: acquiring a sequence of fluorescence speckle images; calculating the image gradient difference between adjacent frames; constructing a three-dimensional interferometric perturbation tensor; and obtaining the principal perturbation mode map through sparse principal component analysis. Based on the principal perturbation mode map, a self-supervised triplet training sample is constructed, and the perturbation value is used as a pixel-level weighting factor to train an image patch encoder. The trained encoder is used to extract image patch features, which are then matched with a response feature dictionary to generate a response probability map. The perturbation spectral entropy of each time-series map is calculated, and its reciprocal is used for weighted fusion to generate a fused response probability map. The result is output through mask screening and response integral calculation, using a fitting method. This invention effectively enhances the local immune response recognition capability, improves the adaptability of feature extraction under perturbation, and enhances the stability of response fusion, achieving high-precision quantitative fluorescence immunoassay.
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