Prediction method, device and equipment for endometrial cancer based on pathological deep learning and storage medium

Through a pathological deep learning method, combining images, text and genetic data, extracting and fusing features, and using multi-layer perceptrons for prediction, the problem of delayed diagnosis of endometrial cancer in the prior art is solved, and the effect of early prediction and reducing surgical operations is achieved.

CN119724562AInactive Publication Date: 2025-03-28THE FIRST AFFILIATED HOSPITAL OF XIAMEN UNIV +1
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Patent Information

Application Number
CN202510224991.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-27
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is often discovered in the middle and late stages of the disease when diagnosing endometrial cancer, resulting in the missed optimal treatment time.

Method used

Using a method based on pathological deep learning, the endometrium image data, historical clinical diagnosis text and gene data are obtained, and the characteristics of the image, text and gene data are extracted using technologies such as convolutional neural network, Transformer model and PCA, and the characteristics of the image, text and gene data are fused. Multi-layer perceptrons are used to predict, and the prediction results of endometrial cancer are output.

Benefits of technology

Early prediction of endometrial cancer is achieved, the frequency of invasive surgical operations is reduced, and the accuracy and timeliness of diagnosis are improved.

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Abstract

The invention provides an endometrial cancer prediction method, device and equipment based on pathological deep learning, and a storage medium, and the method comprises the steps: obtaining the image data of endometrium, carrying out the spatial feature extraction of the image data of endometrium through employing a convolutional neural network, carrying out the analysis of the lesion form, tissue density and abnormal region of the spatial features, and obtaining the prediction result of the endometrial cancer. Generating an image vector; the method comprises the following steps: acquiring a historical clinical diagnosis text, and extracting a text vector associated with endometrial cancer by using a Transform model; obtaining candidate genes associated with endometrial cancer in the gene data, analyzing the candidate genes by using PCA (Principal Component Analysis) to extract mutation site information and variation types, and generating gene vectors according to the mutation site information and the variation types; and fusing the image vector, the text vector and the gene vector to generate a fusion feature, predicting the fusion feature by using a pre-trained multi-layer perceptron, and outputting a prediction result of the endometrial cancer. Early prediction of endometrial cancer is realized, and invasive surgical operations are reduced.
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