InnerLogic-Surg: An Open Surgical Foundation Model via Continual Pretraining
Researchers present InnerLogic-Surg, an open family of surgical vision encoders (21M–300M parameters) obtained by continually pretraining a transformer on millions of surgical images from nearly 3,000 hours of video spanning over fifteen procedure types. The method inherits DINOv3's pretraining stage and adds a learned feature-upsampling module for efficient high-resolution dense prediction. Across 20 surgical tasks including workflow, instrument and anatomy recognition, segmentation, detection, and depth estimation, the largest model achieves the best linear-probe performance under the same architecture. The work demonstrates that surgical continual pretraining yields transferable representations that generalize across procedures and task types, offering a scalable alternative to procedure-specific approaches.