A cGAN method with edge-filtered combined inputs generates synthetic polyp images from normal colonoscopy frames to augment training data and improve detection performance.
Integrating online and offline 3D deep learning for automated polyp detection in colonoscopy videos
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Region-based deep CNN with transfer learning and post-learning methods achieves better polyp detection performance than prior systems on large colonoscopy image and video databases.
citing papers explorer
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Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance
A cGAN method with edge-filtered combined inputs generates synthetic polyp images from normal colonoscopy frames to augment training data and improve detection performance.
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Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches
Region-based deep CNN with transfer learning and post-learning methods achieves better polyp detection performance than prior systems on large colonoscopy image and video databases.