Cell image detection method based on transfer learning

A technology of transfer learning and image detection, which is applied in the field of cell image detection based on transfer learning, can solve problems such as difficulty in labeling, size dependence, and lack of data sets, and achieve the effect of reducing difficulty in labeling, improving accuracy and recall rate

CN113327233APending Publication Date: 2021-08-31北京理工大学重庆创新中心 +1
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2021-08-31

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Abstract

The invention provides a cell image detection method based on transfer learning, and the method comprises the steps: collecting a cell image through a Fourier laminated microscopic imaging system, carrying out fusion through frequency spectrum iteration, obtaining a large-view-field and high-resolution cell image, constructing a cell density estimation network through VGG and FPN network models, marking a cell center position in the cell image to obtain a cell density map, inputting the cell density map into a training model, constructing a cell detection network model by taking the trained network model as a backbone network, carrying out transfer learning, obtaining a cell detection map, inputting the cell detection map into the cell detection network model, extracting a candidate region by adopting an RPN, and carrying out position regression and classification on cells through a regression device and a classifier to finally obtain a cell prediction result. According to the method, based on transfer learning, the network model of common features of the similar data sets can be extracted through transfer learning training, the problem of insufficient training samples is solved, and meanwhile, the accuracy of model output is ensured.
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Description

technical field

[0001] The invention relates to the technical field of cell image detection, in particular to a cell image detection method based on migration learning. Background technique

[0002] With the advent of the information age, in medicine, the collection, storage and processing of images can be realized with the help of computer systems. Digital medical images make data storage more convenient and safe, and at the same time promote the development of digital image processing. With the advancement of the times and technology, the processing of medical images is developing in the direction of digitization, automation and intelligence.

[0003] Microscopic examination is a detection method widely used in clinical medicine. The number and morphological changes of cells in the sample to be tested can provide a lot of useful information for clinical diagnosis. In order to improve the accuracy of diagnosis, it is generally necessary to inspect a large sample area when ...

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Embodiment Construction

[0031] In the following description, the technical solutions in the embodiments of the present invention are clearly and completely described. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0032] Embodiments of the present invention provide a cell image detection method based on migration learning, such as figure 1 As shown, the method includes the following steps:

[0033] A cell image detection method based on migration learning, characterized in that, comprising the steps of:

[0034] S1: Collect cell images, sample the low-resolution intensity images of the sample through the Fourier stack microscopic imaging system, obtain digital image data that can be directly processed by th...