Synthetic Tumor

Synthetic tumor generation uses computational methods, including generative adversarial networks (GANs), diffusion models, and cellular automata, to create realistic artificial tumors in medical images. This addresses the scarcity and annotation challenges of real tumor data, improving the training of AI models for accurate tumor detection and segmentation, particularly in early-stage cancers and rare tumor types. Current research focuses on optimizing synthetic tumor characteristics (size, boundary definition) and developing generalizable models applicable across various organs and imaging modalities to enhance AI performance and robustness. The resulting improvements in AI-driven cancer diagnosis and treatment planning hold significant promise for improving patient outcomes.