From Pixel to Cancer: Cellular Automata in Computed Tomography

arXiv:2403.06459v1 Announce Type: cross
Abstract: AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in medical images, which can greatly diversify the data and annotations for AI training. However, current tumor synthesis approaches are not applicable across different organs due to their need for specific expertise and design. This paper establishes a set of generic rules to simulate tumor development. Each cell (pixel) is …

arxiv cancer cellular cs.cv eess.iv pixel type