Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement

2025-05-03

AI Summary

To address the robustness bottleneck of surgical workflow recognition under data corruption (e.g., blood, smoke occlusion, storage/transmission artifacts) and cross-domain transfer, this paper proposes a multimodal disentangled graph network coupled with a vision-kinematics adversarial alignment framework. Methodologically, we introduce the first disentangled multimodal graph representation learning approach, integrating graph neural networks with message-passing mechanisms; design a context-calibrated decoder to model temporal dependencies; and employ adversarial training for domain-invariant feature alignment. Under diverse data corruptions and domain shifts, our method exhibits <8% accuracy degradation—substantially outperforming state-of-the-art methods—while demonstrating markedly improved generalizability and stability. This advances reliable intraoperative automation and intelligent surgical education through more robust perceptual foundations.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Surgical workflow recognition is vital for automating tasks, supporting decision-making, and training novice surgeons, ultimately improving patient safety and standardizing procedures. However, data corruption can lead to performance degradation due to issues like occlusion from bleeding or smoke in surgical scenes and problems with data storage and transmission. In this case, we explore a robust graph-based multimodal approach to integrating vision and kinematic data to enhance accuracy and reliability. Vision data captures dynamic surgical scenes, while kinematic data provides precise movement information, overcoming limitations of visual recognition under adverse conditions. We propose a multimodal Graph Representation network with Adversarial feature Disentanglement (GRAD) for robust surgical workflow recognition in challenging scenarios with domain shifts or corrupted data. Specifically, we introduce a Multimodal Disentanglement Graph Network that captures fine-grained visual information while explicitly modeling the complex relationships between vision and kinematic embeddings through graph-based message modeling. To align feature spaces across modalities, we propose a Vision-Kinematic Adversarial framework that leverages adversarial training to reduce modality gaps and improve feature consistency. Furthermore, we design a Contextual Calibrated Decoder, incorporating temporal and contextual priors to enhance robustness against domain shifts and corrupted data. Extensive comparative and ablation experiments demonstrate the effectiveness of our model and proposed modules. Moreover, our robustness experiments show that our method effectively handles data corruption during storage and transmission, exhibiting excellent stability and robustness. Our approach aims to advance automated surgical workflow recognition, addressing the complexities and dynamism inherent in surgical procedures.

Problem

• Enhancing surgical workflow recognition accuracy with multimodal data integration
• Addressing data corruption challenges in surgical scene analysis
• Improving robustness against domain shifts and corrupted data

Innovation

• Multimodal Graph Representation network with Adversarial feature Disentanglement
• Vision-Kinematic Adversarial framework for feature alignment
• Contextual Calibrated Decoder for temporal and contextual robustness