A deep learning-based cascade algorithm for pancreatic tumor segmentation

HIGHLIGHTS

• What: Inspired by the doctor`s diagnostic process, the authors propose a cascaded automatic neural algorithm for pancreatic tumor segmentation, which consists of two stages to gradually segment tumor targets. This problem results in the same impact of input features on the model, causing the model to be unable to focus directly on the target and reducing the rate of convergence of the model. The authors design multiple focusing modules to gradually detect and eliminate false negative and false positive interference. The authors find that the low proportion of small-scale pancreatic tumors in the Loss function . . .
  

   

  

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