Patent ID: 11941807
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. The medical image processing method according to claim 8, wherein the training the segmentation neural network and the encoding neural network according to a Deiss loss function and a cross-entropy loss function comprises:
calculating a Deiss loss value according to the Deiss loss function based on a real segmentation label and a segmentation label of the first feature outputted by the segmentation neural network;
calculating a cross-entropy loss value according to the cross-entropy loss function based on the real segmentation label and the segmentation label of the first feature outputted by the segmentation neural network; and
performing training according to a preset threshold based on the Deiss loss value and the cross-entropy loss value,
the Deiss loss function dice and the cross-entropy loss function ce being respectively expressed as:, ℒ
      dice
    
    =
    
      1
      -
      
        
          2
          ⁢
          
            Σ
            i
          
          ⁢
          
            s
            i
          
          ×
          
            q
            i
          
        
        
          
            
              Σ
              i
            
            ⁢
            
              s
              i
            
          
          +
          
            
              Σ
              i
            
            ⁢
            
              q
              i
            
          
        
      
    
  

  
    
      ℒ
      ce
    
    =
    
      
        -
        
          1
          V
        
      
      ⁢
      
        
          ∑
          i
        
        ⁢
        
          (
          
            
              
                s
                i
              
              ×
              
                log
                ⁡
                
                  (
                  
                    q
                    i
                  
                  )
                
              
            
            +
            
              
                (
                
                  1
                  -
                  
                    s
                    i
                  
                
                )
              
              ×
              
                log
                ⁡
                
                  (
                  
                    1
                    -
                    
                      q
                      i
                    
                  
                  )
                
              
            
          
          )
        
      
    
  

si representing a real segmentation label of an ith pixel in the medical image, qi representing a prediction segmentation label of the ith pixel outputted by the segmentation neural network, and V representing a total quantity of pixels comprised in the medical image.