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Anonymous Image Data Generation from Gastric X-Ray Images for Improving Gastritis Recognition Performance

This paper presents an anonymous gastritis image generation method for improving gastritis recognition performance. We realize the generation of realistic gastritis images by considering label information. Experimental results showed that anonymous images generated by our method had a potential for a gastritis recognition task. Concretely, the recognition performance of a classifier constructed with the anonymous images outperformed the performance of the conventional image generation method-based classifier.