Method for classifying cells

Abstract

The invention discloses a method for classifying cells, comprising the following steps of: acquiring a sample set of k-type cell images, wherein each cell image sample set comprises Nk-numbered cell image samples; forming a k-type subspace from the sample set of k-type cell images; carrying out scale conservation on each cell image sample to obtain the processed cell image samples; extracting a first visual feature vector from the processed cell image samples, presenting the k-type subspace Ik to be a set of the first visual feature vector, namely, building a target fitting energy function; acquiring a corresponding dictionary of the k-type subspace Ik to obtain the test object X and adopting the dictionary delta k to fit respectively, wherein the object fitting energy function reaches the fitting factor wk which is corresponding to the minimum value; and obtaining the residual error rk when the test object X is fitted, selecting the minimum value of the residual error rk, and making the subspace serial number k corresponding to the minimum value to be the cell category to which the test object X belongs. By adopting the method disclosed by the invention, the generalization capability of the model and the accuracy of cell classification can be improved, and a higher classification rate can be got through experimental verification.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps improve the performance of machine learning models used for identifying cells or classing them accurately based on their characteristics such as size shape color texture etcetera. It also allows researchers to verify that high-quality results are achieved with this approach compared to existing methods without actually testing it out over time.

Problems solved by technology

Technological Problem addressed in this patented technical problem includes improving the performance of automated microscopes used during diagnoses (particularly when trying to identify specific diseases), particularly those associated with autism spectrum syndrome or multiple sclerosis (MS). Current methods are often imprecise because they rely heavily upon human judges who have their own limitations such as lack reliability over time. There may be some cases where these techniques can lead to incorrect results even if only trained experts provide them feedback.

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