CentroidX-GAN: A Centroid-Based Text-to-Chest X-Ray Image Synthesis Model Using Generative Adversarial Network
摘要
Recent advancements in text-to-image synthesis, particularly through Generative Adversarial Networks (GANs), have garnered significant interest. This paper explores the utilization of GANs for this purpose, examining a centroid-based text-to-image synthesis model. Challenges such as mode collapse and semantic gaps are addressed, alongside potential solutions and future research directions. The paper concludes by emphasizing the broad applications of text-to-image synthesis, encompassing content creation and assistive technologies. Our research investigates the application of GANs for synthesizing chest X-ray images from corresponding medical reports. This approach can greatly improve medical diagnosis and treatment processes.