Tumor Growth Simulation and Prognosis Prediction via GANs for Precision Medicine

Abstract

This study proposes a method for tumor growth simulation and prognosis prediction based on the generative adversarial network (GAN) framework. Through comparative experiments with variational autoencoder (VAE), deep convolutional generative adversarial network (DCGAN), conditional generative adversarial network (CGAN) and self-attention generative adversarial network (SAGAN), it is verified that GAN is good at generating image quality, Significant advantages in structural fidelity and detail restoration. Experimental results show that the GAN model performs best in indicators such as structural similarity (SSIM) and mean square error (MSE), and can effectively simulate real tumor growth characteristics, providing more accurate data support for clinical diagnosis and personalized treatment. This method demonstrates the potential of GAN in medical image generation and provides new technical means for future cancer research and precision medicine.