Login / Signup

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

Xiaoli WuHao WuShouliang MiaoGuoquan CaoHuang SuJie PanYilun Xu
Published in: BMC medical informatics and decision making (2024)
ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.
Keyphrases
  • deep learning
  • optical coherence tomography
  • cell migration
  • convolutional neural network
  • artificial intelligence
  • machine learning
  • lymph node
  • computed tomography
  • pet ct
  • image quality
  • ionic liquid