4 results about "Age and gender" patented technology

The invention relates to an age and gender prediction method based on a multi-view image and application. The method comprises the following steps: constructing an age and gender prediction model, and establishing an age prediction optimization model; a plurality of images of the face at different angles are collected and processed, and a training data set is established together with age and gender labels; inputting the training data into an age and gender prediction model for training, and optimizing an output result by an age prediction optimization model; after a plurality of images of different angles of the face are collected and processed, the images are input into the trained age and gender prediction model, the corresponding gender and a predicted age coarse value are output, the predicted age coarse value is optimized through an age prediction optimization model, and the optimized predicted age and gender are obtained; the method is used for predicting the age and the gender of a collected person after a face image is collected. According to the invention, the accuracy of age and gender prediction is significantly improved; dynamic adjustment is achieved, the accuracy of age prediction is continuously improved, and time and resource cost are greatly saved; and more personalized services can be conveniently provided for clients.

The application discloses a gender and age-based test item reference interval construction system, which comprises the following steps: obtaining a set of health examination test data from a laboratory information system; removing duplicates and performing data screening on the set of health examination test data to obtain a set of candidate health examination test data; grouping the set of candidate health examination test data based on age and gender to obtain a grouping result; performing outlier processing and data distribution description on a data subset of each candidate health examination test data in the grouping result; determining a reference interval of each data subset of the candidate health examination test data by using a big data algorithm to obtain a plurality of reference intervals; and constructing a continuous reference interval based on the plurality of reference intervals. In this way, the test item reference interval is constructed based on real-world test data, which improves feasibility and reduces cost.

The present invention discloses a method and system for estimating age and gender from cephalometric X-ray images based on a latent variable model. The method includes: obtaining a cephalometric X-ray image of a target to be detected; preprocessing the cephalometric X-ray image and inputting it into an age and gender recognition model to generate an age and gender estimate for the target to be detected. Among them, the age and gender recognition model includes a feature extraction unit, and the feature extraction unit is obtained by pruning the feature extractor of the pre-trained VGG16. The specific process includes: obtaining the dependency relationships between the convolutional layers in the feature extractor of the pre-trained VGG16, and establishing a dependency graph; grouping the parameters in the dependency graph, and the parameters in each group have the same dependency relationship; for each group, calculating the importance of each parameter in the group; using the importance of each parameter, calculating the joint importance of each group; using the joint importance of each group to prune the group. The present invention greatly reduces the computational complexity and improves the processing speed.

The application provides an anatomic structure segmentation-based medical age and gender prediction framework and an explanation system, comprising: a data preprocessing and standardization module for cleaning, enhancing and normalizing input medical images; an age and gender feature extraction and prediction module for constructing and training a deep learning model to extract features from the medical images processed by the data preprocessing and standardization module and predict age or gender labels; a model explanation extraction module under structural constraints for taking medical prior segmentation results as structural constraints, generating model decision explanations aligned with medical structures based on intermediate layer feature maps and prediction outputs obtained by the age and gender feature extraction and prediction module through a dual-view technology path; and an explanation reliability verification module for quantitatively verifying the causal fidelity and rationality of the explanation results of the model explanation extraction module under structural constraints through a gradual intervention strategy, so as to improve the quality and readability of the explanation.