Can LLM-Generated Text Empower Surgical Vision-Language Pre-training?

2026-04-20

AI Summary

This work addresses the scarcity and high cost of expert-annotated multimodal data in surgical settings, which hinders the advancement of vision–language pretraining. To overcome this limitation, the authors propose SurgLIME, a framework that leverages large language models (LLMs) to automatically generate textual descriptions for surgical videos, thereby constructing LIME—a large-scale, unlabeled multimodal dataset. SurgLIME employs a LoRA-finetuned dual-encoder architecture combined with contrastive learning, automated confidence estimation, and dynamic weighting to achieve robust cross-modal alignment despite noisy text supervision. Experiments demonstrate that SurgLIME exhibits strong zero-shot transfer performance on the AutoLaparo and Cholec80 benchmarks while maintaining competitive visual representation capabilities under linear probing evaluation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Recent advancements in self-supervised learning have led to powerful surgical vision encoders capable of spatiotemporal understanding. However, extending these visual foundations to multi-modal reasoning tasks is severely bottlenecked by the prohibitive cost of expert textual annotations. To overcome this scalability limitation, we introduce \textbf{LIME}, a large-scale multi-modal dataset derived from open-access surgical videos using human-free, Large Language Model (LLM)-generated narratives. While LIME offers immense scalability, unverified generated texts may contain errors, including hallucinations, that could potentially lead to catastrophically degraded pre-trained medical priors in standard contrastive pipelines. To mitigate this, we propose \textbf{SurgLIME}, a parameter-efficient Vision-Language Pre-training (VLP) framework designed to learn reliable cross-modal alignments using noisy narratives. SurgLIME preserves foundational medical priors using a LoRA-adapted dual-encoder architecture and introduces an automated confidence estimation mechanism that dynamically down-weights uncertain text during contrastive alignment. Evaluations on the AutoLaparo and Cholec80 benchmarks show that SurgLIME achieves competitive zero-shot cross-modal alignment while preserving the robust linear probing performance of the visual foundation model. Dataset, code, and models are publicly available at \href{https://github.com/visurg-ai/SurgLIME}{https://github.com/visurg-ai/SurgLIME}.

Problem

• surgical vision-language pre-training
• expert textual annotations
• LLM-generated narratives
• noisy text
• medical priors

Innovation

• Vision-Language Pre-training
• LLM-generated text
• Surgical AI
• Noisy narrative alignment
• Parameter-efficient learning