A PREDICTION MODEL FOR TURNER SYNDROME BASED ON FEW-SHOT LEARNING AND MULTISCALE RESIDUAL NETWOEK
A prediction model is proposed for improving the diagnosis efficiency of Turner syndrome(TS)based on a multiscale residual network(MRN)and few-shot learning.TS facial images were pre-processed to obtain the main facial areas.A multiscale residual block(MRB)with multilevel attention mechanisms(MAM)was designed.The MRB was implemented by integrating the residual structure of multi-scale convolution kernels,and the MAM was used to learn feature channel relationships and the importance of different convolution kernels.The MRN was built using the MRB.The few-shot learning was utilized to train the MRN.The experimental results demonstrate that the prediction model can improve the diagnostic accuracy of TS.
万方数据
维普
