Published online Jul 7, 2020. doi: 10.3748/wjg.v26.i25.3650
Peer-review started: February 10, 2020
First decision: March 15, 2020
Revised: April 3, 2020
Accepted: June 18, 2020
Article in press: June 18, 2020
Published online: July 7, 2020
It has been reported that chronic atrophic gastritis (CAG) induce by Helicobacter pylori infection increases the risk of gastric cancer. X-ray examination can evaluate the condition of the stomach for mass screening. On the other hand, there remains a problem that skilled doctors are decreasing.
Researches for the detection of CAG have been conducted, especially, deep learning-based techniques have achieved high recognition performance in general image datasets. However, early works need a large number of labeled images for training.
The study aimed to evaluate the effectiveness of a deep learning technique with a small number of training images with the stomach region annotation.
A total of 815 gastric X-ray images (GXIs) were used in our analysis. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A deep learning model is trained with the stomach region annotations. For the rest of them, the stomach regions are automatically estimated by the learned model. Finally, a model for automatic CAG detection is trained with all GXIs for training.
In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively.
By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.
Our CAG detection method can be trained with data from a small-scale or medium-scale hospital without medical data sharing that having the risk of leakage of personal information.