Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation
Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers. Although representation alignment has improved efficiency in general computer vision, its role within the highly specialized endoscopic image space remains unclear. We introduce REVEAL (Representation-driven Endoscopic Visual Embedding Alignment), the largest generative foundation model for endoscopy to date, trained on GastroNet-5M (GN-5M), a multicenter dataset of 5 million endoscopic frames. Instead of depending on out-of-domain priors, REVEAL employs encoders pretrained directly on the endoscopic distributi...
| Authors | Francisco Caetano, Tim J. M. Jaspers, Haiko Middeljans et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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