We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction.
We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction.
We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction.
We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction.