Image classification method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides an image classification method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a to-be-classified neural image; inputting the neural image into a classification model to obtain a classification result of the neural image output by the classification model; the classification model is obtained by training based on a first sample nerve image and a corresponding sample classification result on the basis of a multi-task learning pre-training model, and the multi-task learning pre-training model is obtained by training based on a second sample nerve image and a sample label under each task corresponding to the second sample nerve image on the basis of an unsupervised pre-training model; the unsupervised pre-training model is obtained based on unsupervised training of the third sample neural image. According to the method and device, the electronic equipment and the storage medium provided by the invention, the data labeling cost is saved, the problem of overfitting of the model is avoided, the performance and generalization of the model on an image classification task are improved, and the accuracy of a classification result is improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, and in particular, to an image classification method, an apparatus, an electronic device and a storage medium. Background technique [0002] In deep learning, the size of the data set directly affects the performance of the deep model. The larger the number of samples, the better the effect of the trained model and the stronger the generalization ability of the model. [0003] At present, most of the classification studies of deep models based on neuroimaging use training methods based on a single supervised task. However, brain neuroimaging datasets are usually small in scale, with only tens or hundreds of cases, and high The high cost of quality annotation makes this supervised training method prone to overfitting, resulting in poor performance of deep models. SUMMARY OF THE INVENTION [0004] The present invention provides an image classification method, device, electronic d...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides an image classification method, device, electronic equipment and storage medium, which are used to solve the defect that the training method of the neuroimage classification model in the prior art is prone to over-fitting

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