# InnerLogic-Surg

Surgical image analysis has long relied on encoders pretrained on natural images or on specialist models trained from scratch on comparatively small annotated datasets. InnerLogic-Surg starts from DINOv3 and applies continual self-supervised pretraining to millions of frames drawn from 11,965 public surgical videos, about 2,900 hours across more than fifteen procedure types, yielding an encoder family of 21M to 300M parameters. Under a frozen-backbone protocol with only a linear readout trained, the family achieves the best linear-probe performance on all 20 tasks spanning workflow, instrument and anatomy recognition, segmentation, detection and monocular depth, with ViT-L leading on 18 axes and ViT-B best on GraSP step recognition and monocular depth.

Surgical image analysis has long relied on encoders pretrained on natural images or on specialist models trained from scratch on comparatively small annotated datasets. InnerLogic-Surg starts from DINOv3 and applies continual self-supervised pretraining to millions of frames drawn from 11,965 public surgical videos, about 2,900 hours across more than fifteen procedure types, yielding an encoder family of 21M to 300M parameters. Under a frozen-backbone protocol with only a linear readout trained, the family achieves the best linear-probe performance on all 20 tasks spanning workflow, instrument and anatomy recognition, segmentation, detection and monocular depth, with ViT-L leading on 18 axes and ViT-B best on GraSP step recognition and monocular depth.