Towards Effective Surgical Representation Learning with DINO Models

Ronald L.P.D. de Jong, Yiping Li, Tim J. M. Jaspers, Romy C. van Jaarsveld, Gino M. Kuiper, Franco Badaloni, Richard van Hillegersberg, Jelle P. Ruurda, Fons van der Sommen, Josien P.W. Pluim, Marcel Breeuwer

Keywords: Anatomy recognition, DINO, Self-supervised learning, Phase recognition

TL;DR: We show that DINOv1–v3 self-supervised models learn strong and scalable surgical representations across multiple datasets and settings.

Abstract: Self-supervised learning (SSL) has emerged as a promising approach to address the limitations of annotated surgical datasets, which are often small, heterogeneous, and expensive to curate. Among SSL methods, self-distillation with no labels (DINO) has achieved state-of-the-art (SOTA) results in natural images, but its applicability to surgical data remains underexplored. In this work, we systematically investigate DINOv1, DINOv2, and DINOv3 for surgical representation learning. We pretrain these models on a large-scale surgical dataset of 4.7M video frames (SurgeNetXL) and evaluate their transferability on downstream tasks including semantic segmentation and surgical phase recognition. Our results demonstrate that in-domain pretraining consistently improves performance across all DINO variants, with DINOv2 and DINOv3 achieving SOTA performance. We further offer practical insights and visualizations highlighting the effectiveness of SSL. Finally, our study delivers ready-to-use DINO-based SSL models and pretraining protocols for surgical AI research, which are publicly available at: https://github.com/rlpddejong/SurgeNetDINO.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Foundation Models

Registration Requirement: Yes

Reproducibility: https://github.com/rlpddejong/SurgeNetDINO

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 83