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Related Experiment Video

Updated: May 29, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes.

Felix Holm^1,2,3, Ghazal Ghazaei⁴, Nassir Navab^5,6

¹Chair for Computer-Aided Medical Procedures, Technical University Munich, Munich, Germany. felix.holm@tum.de.

International Journal of Computer Assisted Radiology and Surgery

|May 28, 2026

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This summary is machine-generated.

ProtoFlow, a novel AI framework, uses dynamic scene graph prototypes for interpretable surgical workflow analysis. This approach enhances AI-assisted surgery by improving data efficiency and model transparency.

Area of Science:

Artificial Intelligence in Medicine
Surgical Workflow Analysis
Computer Vision for Surgery

Background:

AI-assisted surgery requires detailed surgical recognition for advancement.
High annotation costs, data scarcity, and lack of interpretable models hinder AI progress.
Scene graphs offer structured surgical event abstraction but are underutilized.

Purpose of the Study:

Introduce ProtoFlow, a novel framework for interpretable and robust surgical workflow modeling.
Utilize dynamic scene graph prototypes to capture complex surgical procedures.
Address limitations in current AI-assisted surgery approaches.

Main Methods:

Employ a graph neural network (GNN) encoder-decoder architecture.
Integrate self-supervised pretraining for representation learning.

Keywords:

Graph neural networks Prototype learning Scene graphs Surgical data science

Related Experiment Videos

Last Updated: May 29, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Implement a prototype-based fine-tuning stage to discover and refine clinically meaningful patterns.

Main Results:

ProtoFlow outperforms standard GNN baselines on the CAT-SG dataset.
Demonstrates exceptional robustness in few-shot learning scenarios, performing well with minimal data.
Learned prototypes identify distinct surgical sub-techniques and provide interpretable insights into workflow deviations.

Conclusions:

ProtoFlow advances AI systems for surgery through robust representation learning and explainability.
Facilitates the development of transparent, reliable, and data-efficient AI tools.
Accelerates clinical adoption for surgical training, decision support, and workflow optimization.