Eyelid tumor digital pathological section image multi-classification method based on deep learning

A technology for pathological sectioning and eyelid tumors, applied in image analysis, image enhancement, image data processing, etc., to achieve high accuracy
CN113449785AActive Publication Date: 2021-09-28ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Current Assignee / Owner
ZHEJIANG UNIV
Publication Date
2021-09-28

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Abstract

The invention discloses an eyelid tumor digital pathological section image multi-classification method based on deep learning. The method comprises steps of scanning eyelid tumor pathological sections classified by known lesion categories to obtain an image construction training set; performing data enhancement and normalization processing, constructing a three-layer cascaded tumor digital pathological section diagnosis network, training the tumor digital pathological section diagnosis network by using an enhanced training set, performing prediction processing, generating a probability thermodynamic diagram, and performing lesion category detection. The method can effectively visualize the position and lesion type of the tumor in the full-field digital slice to assist in diagnosis, perform preliminary screening and prompting of lesion areas, change the attention of the network to channels and improve the network performance.
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Description

technical field

[0001] The present invention relates to a multi-classification method for eyelid images in the field of deep learning, computer vision and ophthalmic tumors, in particular to a multi-classification method for digitized pathological slice images of eyelid tumors based on deep learning, which uses deep learning technology to digitize eyelid tumors A method for detecting and analyzing pathological slice images. Background technique

[0002] Computer vision is a kind of artificial intelligence technology, which refers to the use of computers to simulate human vision, which is the "seeing" in artificial intelligence. In terms of technical process, it is divided into three parts: target detection, target recognition, and behavior recognition. According to the target type of recognition, it can be divided into image recognition, object recognition, face recognition, text recognition and so on. In the field of intelligent robots, computer vision can perform feature...

Claims

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