A method and system for predicting embryonic growth stages based on self-supervised learning

Through self-supervised learning and multi-core loss function optimization prediction model, the problem of insufficient generalization ability of embryo growth prediction model when facing new blastocyst images is solved, and higher prediction accuracy and robustness are achieved, reducing calculation complexity and labeling costs.

CN119942249BActive Publication Date: 2025-07-22WUHAN MUTUAL UNITED TECH CO LTD
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Patent Information

Application Number
CN202510432149.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-04-08
Publication Date
2025-07-22
Estimated Expiration
2045-04-08

AI Technical Summary

Technical Problem

Existing embryo growth prediction models lack generalization ability when facing brand new blastocyst images, and cannot accurately predict the growth stage of embryos, especially when dealing with outliers and noise.

Method used

Using a self-supervised learning method, a prediction model including an encoder and a decoder is constructed by segmenting the embryo image into several blocks and randomly masking it, and the model parameters are optimized using multi-core loss functions, combining VideoMamba encoder and multiple kernel functions to improve the robustness and generalization capabilities of the model.

Benefits of technology

It improves the prediction accuracy and robustness of the model for blastocyst images, can better handle outliers and noise in complex images, reduces dependence on labeled data, and enhances the generalization ability of the model.

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Abstract

The present invention discloses a method and system for predicting the embryonic growth stage based on self-supervised learning, which includes the following steps: collecting embryonic images at different growth stages as a training set, and dividing each embryonic image in the training set into a number of blocks of the same size; randomly selecting half of the blocks in each embryonic image for masking to obtain a masked training set; constructing a prediction model including an encoder and a decoder, where the encoder extracts a feature sequence from the masked training set, and the decoder reconstructs the embryonic image according to the feature sequence; using multiple weighted combined kernel functions as the loss function of the prediction model, adjusting the weight of each kernel function through cross-validation and gradient descent, and optimizing the parameters of the prediction model by backpropagation to obtain an optimal prediction model; inputting the embryonic image to be predicted into the optimal prediction model to obtain the prediction result of the embryonic growth stage.
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