Clin Endosc.  2020 Mar;53(2):127-131. 10.5946/ce.2020.046.

Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer

Affiliations
  • 1Division of Gastroenterology, Department of Internal Medicine, Soonchunhyang University College of Medicine, Cheonan, Korea
  • 2Division of Gastroenterology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea

Abstract

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion�-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.

Keyword

Artificial intelligence; Convolutional neural networks; Early gastric cancer; Endoscopy; Invasion depth

Figure

  • Fig. 1. Simple example of deep learning convolutional neural network using early gastric cancer detection model.

  • Fig. 2. Examples of gradient-weighted class activation mapping output extracted from each convolutional layer of the trained lesion-based convolutional neural network. The white lines on the first row indicate the actual early gastric cancer regions. The images on the second row represent the activated map extracted from the last convolutional layer of the network.

  • Fig. 3. Example of lesion-based convolutional neural network algorithm with gradient-weighted class activation mapping method. Grad-CAM, gradient-weighted class activation mapping.


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