Synthetic Image Generation for Gastritis Detection Based on Auxiliary Classifier Generative Adversarial Network
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama · 2019
With the development of convolutional neural networks (CNNs), CNN-based methods for medical image analysis have achieved more accurate performance than conventional machine learning methods using hand-crafted features. Although these methods utilize a large number of training images and realize high performance, lack of the training images often occurs in medical image analysis due to several reasons. This paper presents a novel image generation method to construct a dataset for gastritis detection from gastric X-ray images. The proposed method effectively utilizes two kinds of training images (gastritis and non-gastritis images) to generate images of each domain by introducing label conditioning into a generative model. Experimental results using real-world gastric X-ray images show the effectiveness of the proposed method.