Aiming at the problems of insufficient data and low accuracy in predicting the mutation status of brain glioma isocitrate dehydrogenase1(IDH1),a prediction method for IDH1 mutation status of brain glioma is proposed based on auto-encoder vision Transformer(AEViT)and priori knowledge.Firstly,an improved K-Means clustering algorithm was used to label the pseudo-labels for MRI data of glioma without IDH1 status labels,and vision Transformer(ViT)network was used to modify the pseudo labels to obtain the final glioma IDH1 mutation status.In order to avoid inaccur-ate pseudo-label data that affect accuracy of the model,the fruit fly optimization algorithm was used to assign the optim-al weight to the pseudo-label data.Secondly,a classification model AEViT based on Auto-Encoder and Vision Trans-former was proposed,and Auto-Encoder was used to extract MRI features of glioma,and then the features were input into ViT to classify the IDH1 mutation status of glioma.Finally,the prior knowledge based on glioma location informa-tion was added to the model,which achieved a prediction accuracy of 99.01%.The experimental results show that this method is superior to other existing models,and can realize glioma data augmentation and non-invasive and accurate preoperative prediction of glioma IDH1 mutation status,thereby assisting the diagnosis and treatment process.
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