SurgPub-Video: A Comprehensive Surgical Video Framework for Enhanced Surgical Intelligence in Vision-Language Model
Yaoqian Li, Xikai Yang, Dunyuan Xu, Yang YU, Litao Zhao, Xiaowei Hu, Jinpeng Li, Pheng-Ann Heng
Abstract
Vision-Language Models (VLMs) have shown significant potential in surgical scene analysis, yet existing models are limited by frame-level datasets and lack high-quality video data with procedural surgical knowledge. To address these challenges, we make the following contributions: (i) SurgPub-Video, a comprehensive dataset of over 3,000 surgical videos and 25 million annotated frames across 11 specialities, sourced from peer-reviewed clinical journals, (ii) SurgLLaVA-Video, a specialized VLM for surgical video understanding, built upon the TinyLLaVA-Video architecture that supports both video-level and frame-level inputs, and (iii) a video-level surgical Visual Question Answering (VQA) benchmark, covering diverse 11 surgical specialities, such as vascular, cardiology, and thoracic. Extensive experiments, conducted on the proposed benchmark and three additional surgical downstream tasks (action recognition, skill assessment, and triplet recognition), show that SurgLLaVA-Video significantly outperforms both general-purpose and surgical-specific VLMs with only three billion parameters.
BibTeX
@inproceedings{aaai2026_surgpubvideoacom,
title = {SurgPub-Video: A Comprehensive Surgical Video Framework for Enhanced Surgical Intelligence in Vision-Language Model},
author = {Yaoqian Li and Xikai Yang and Dunyuan Xu and Yang YU and Litao Zhao and Xiaowei Hu and Jinpeng Li and Pheng-Ann Heng},
booktitle = {AAAI 2026},
year = {2026}
}