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SurgFusion-Net: Diversified Adaptive Multimodal Fusion Network for Surgical Skill Assessment
arXiv – CS AI|Runlong He, Freweini M. Tesfai, Matthew W. E. Boal, Nazir Sirajudeen, Dimitrios Anastasiou, Jialang Xu, Mobarak I. Hoque, Philip J. Edwards, John D. Kelly, Ashwin Sridhar, Abdolrahim Kadkhodamohammadi, Dhivya Chandrasekaran, Matthew J. Clarkson, Danail Stoyanov, Nader Francis, Evangelos B. Mazomenos||4 views
🤖AI Summary
Researchers developed SurgFusion-Net, a multimodal AI system for assessing surgical skills in robotic-assisted surgery. The system introduces new clinical datasets and fusion techniques that outperform existing baselines, addressing the domain gap between simulation and real clinical environments.
Key Takeaways
- →SurgFusion-Net introduces Divergence Regulated Attention (DRA) for multimodal fusion in surgical skill assessment.
- →Two new clinical datasets were created: RAH-skill with 279,691 RGB frames and RARP-skill with 70,661 RGB frames.
- →The system addresses the significant domain gap between controlled simulation and real clinical surgical environments.
- →Performance improvements include 0.02-0.04 SCC gains on JIGSAWS benchmark and 0.0538-0.0493 gains on clinical datasets.
- →The approach fuses three modalities including RGB video, optical flow, and tool segmentation for enhanced accuracy.
#surgical-ai#multimodal-fusion#robotic-surgery#medical-ai#computer-vision#deep-learning#healthcare#surgical-training
Read Original →via arXiv – CS AI
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