Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation

2024-09-30

Neural Information Processing Systems

AI Summary

This work addresses key challenges in surgical video-language pretraining (VLP): domain knowledge gaps, scarcity of multimodal surgical data, loss of textual information in surgical lecture videos, and difficulties in spatiotemporal alignment. To this end, we propose PeskaVLP, a hierarchical knowledge-enhanced framework. Methodologically, it introduces: (1) an LLM-driven surgical concept refinement module that explicitly injects domain-specific semantic knowledge; (2) a DTW-guided cross-modal procedural-level alignment mechanism to mitigate temporal asynchrony between video and text; and (3) an integrated learning strategy combining visual self-supervision, hard negative mining, and multi-granularity knowledge distillation. Evaluated on multiple surgical understanding and cross-modal retrieval benchmarks, PeskaVLP achieves substantial gains in zero-shot transfer performance and learns generalized, anatomy- and procedure-aware visual representations.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

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

Abstract

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data. This study aims to bridge the gap by addressing issues regarding textual information loss in surgical lecture videos and the spatial-temporal challenges of surgical VLP. We propose a hierarchical knowledge augmentation approach and a novel Procedure-Encoded Surgical Knowledge-Augmented Video-Language Pretraining (PeskaVLP) framework to tackle these issues. The knowledge augmentation uses large language models (LLM) for refining and enriching surgical concepts, thus providing comprehensive language supervision and reducing the risk of overfitting. PeskaVLP combines language supervision with visual self-supervision, constructing hard negative samples and employing a Dynamic Time Warping (DTW) based loss function to effectively comprehend the cross-modal procedural alignment. Extensive experiments on multiple public surgical scene understanding and cross-modal retrieval datasets show that our proposed method significantly improves zero-shot transferring performance and offers a generalist visual representation for further advancements in surgical scene understanding.The code is available at https://github.com/CAMMA-public/SurgVLP

Problem

• Bridges knowledge domain gap in surgical video-language pretraining.
• Addresses textual information loss in surgical lecture videos.
• Tackles spatial-temporal challenges in surgical video-language pretraining.

Innovation

• Hierarchical knowledge augmentation with LLMs
• Procedure-Encoded Surgical Knowledge-Augmented VLP
• DTW-based loss for cross-modal alignment