You can train powerful medical imaging models without expensive manual annotation by simulating realistic training data from existing 3D datasets—FleXray segments full-body X-rays and works on real clinical data despite being trained entirely on synthetic images.
FleXray is a generalist AI model that segments 60 anatomical structures in clinical X-rays across the entire body. Rather than manually labeling thousands of X-rays, the researchers built a physics-based simulator that generates realistic synthetic X-rays from existing 3D CT scans, then trained the model on these simulations.