Tumor Synthesis

Tumor synthesis uses computational methods to generate artificial tumors in medical images, primarily CT scans, to address the scarcity of annotated data for training AI models in cancer detection and segmentation. Current research focuses on developing both modeling-based and learning-based approaches, including generative adversarial networks and diffusion models, to create realistic and generalizable synthetic tumors across various organs and stages of development. This technique significantly improves the performance of AI algorithms for early cancer detection, particularly in challenging cases with limited real-world data, ultimately enhancing diagnostic accuracy and potentially improving patient outcomes.