Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation

TL;DR - REVEAL is a diffusion-transformer generative foundation model for endoscopy, trained on GastroNet-5M (5M multicenter frames), that aligns diffusion latents with in-domain endoscopic encoders instead of out-of-domain priors. It matters because it cuts the compute barrier to building specialized clinical imaging tools while doubling as a strong feature extractor.

• Uses representation alignment with encoders pretrained on the endoscopic distribution, arguing out-of-domain priors fail to preserve fine textures and anatomical structure in this specialized image space.
• Claimed to be the largest endoscopy generative foundation model to date; trained on the multicenter GN-5M dataset of 5 million frames.
• As a feature extractor it is competitive with, and sometimes exceeds, classification-tuned endoscopic foundation models (EndoViT, Endo-FM), with reported robustness under realistic imaging corruptions.
• Supports high-fidelity synthesis plus latent-space edits (inpainting, outpainting) with structural coherence, and is positioned as a backbone for conditional synthesis, segmentation, and OOD detection.