Improving gastric lesion detection with synthetic images from diffusion models

In the application of deep learning for gastric cancer detection, the quality of the data set is as important as, if not more, the design of the network architecture. However, obtaining labeled data, especially in fields such as medical imaging to detect gastric cancer, can be expensive and challenging. This scarcity is exacerbated by stringent privacy regulations and the need for annotations by specialists. Conventional methods of data augmentation fall short due to the complexities of medical imagery. In this paper, we explore the use of diffusion models to generate synthetic medical images for the detection of gastric cancer. We evaluate their capability to produce realistic images that can augment small datasets, potentially enhancing the accuracy and robustness of detection algorithms. By training diffusion models on existing gastric cancer data and producing new images, our aim is to expand these datasets, thereby enhancing the efficiency of deep learning model training to achieve better precision and generalization in lesion detection. Our findings indicate that images generated by diffusion models significantly mitigate the issue of data scarcity, advancing the field of deep learning in medical imaging.