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
Record details
Published: 7 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation,” evidence record 17807, https://ethics.ai/record/17807 (originally published by arXiv).
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