Patent ID: 11915401
Assignee: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for a multitask medical image synthesis, comprising:
training, based on a generative adversarial mechanism, an apriori guidance network for the multitask medical image synthesis; and
inputting source modal image data and an apriori feature, the apriori feature being a deep feature of a target modal image, into a trained generator to synthesize the target modal image;
wherein the apriori guidance network comprises the generator and a discriminator, wherein the generator comprises an apriori guidance module configured to convert an input feature map into the target modal image pointing to a target domain according to the apriori feature; and
wherein the generator is configured to generate a corresponding target domain image by taking the apriori feature of the target modal image and the source modal image data as an input; and the discriminator is configured to discriminate an authenticity of the corresponding target domain image outputted by the generator;
wherein for the input feature map X having a size of H×W×C and the apriori feature Z having a length of L, the apriori guidance module is configured to generate the target modal image pointing to the target domain by:
normalizing the input feature map X to obtain a normalized feature map X′, a normalization is expressed as:, X
    ′
   
   =
   
    
     X
     -
     
      Mean
      (
      X
      )
     
    
    
     STD
     ⁡
     (
     X
     )
    
   
  
  ,
 

processing the apriori feature Z by independent full connection operations f1 and f2 respectively to obtain a scaled vector λ and a spatial biased vector b both having a length of C, expressed as:

λ=f1(Z),

b=f2(Z),

and obtaining an output feature map pointing to the target domain by multiplying the normalized feature map X′ by the scaled vector λ along a channel direction and plus the spatial biased vector b along the channel direction, expressed as:

Y=λ·X′+b, 

wherein Mean represents a mean value, and STD represents a variance.