Patent ID: 11900592
Assignee: PING AN TECHNOLOGY (SHENZHEN) CO., LTD
Field: Medical technology (Instruments)
Classification: CPC G  A | IPC A  G

Claim 10:
11. The method according to claim 1, wherein:
the Graph-ResNet includes six graph-based convolutional layers and shortcut connections between every two layers;
each of the six graph-based convolutional layers hpl+1 is defined as hpl+1=w0hpl+Σp′∈N(p)w1hp′l, wherein hpl is a local feature vector attached to the vertex p at a layer l of the Graph-ResNet, and w0 and w1 are learned parameters with w1 being shared by all edges;
the Graph-ResNet includes a vertex level loss function Lvertet, wherein Lvertet=−ΣpΣkK yk,pν log (G(h0)k,p),G(h0) is a softmax output of the Graph-ResNet at every vertex, a vertex label yν is a one-hot encoding of ŷpν inferred from a labeled mask, background voxels are labeled as 0, pancreas voxels are labeled as 1, voxels for the segmented pancreatic mass are labeled with a number greater than 1, the vertex p is labeled using a maximum value of the voxels in its corresponding zone Z(p), and, y
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      ⁢
      
        
          y
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  ;

 and
the fully connected global classification layer includes a loss function Lglobal, wherein Lglobal=−ΣkKykg log(H(hνp)k),H(hνp) is a softmax output of the fully connected global classification layer, and yg is a patient level mass/disease label.