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Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

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Self-knowledge distillation for surgical phase recognition.

Jinglu Zhang¹, Santiago Barbarisi¹, Abdolrahim Kadkhodamohammadi²

¹Medtronic Digital Surgery, 230 City Road, London, UK.

International Journal of Computer Assisted Radiology and Surgery

|June 20, 2023

Summary

This summary is machine-generated.

This study introduces a self-knowledge distillation framework to enhance surgical phase recognition models. The method improves performance without adding complexity, even with reduced training data.

Keywords:

Knowledge distillation Self-supervised learning Surgical data science Surgical phase recognition

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Area of Science:

Computer Vision
Medical AI
Surgical Technology

Background:

Surgical phase recognition models typically advance by increasing network depth.
Current state-of-the-art (SOTA) models can be optimized further without added complexity.

Purpose of the Study:

To propose a novel self-knowledge distillation framework for surgical phase recognition.
To enhance existing SOTA models without requiring additional complexity or annotations.

Main Methods:

Implemented a self-knowledge distillation framework within an encoder-decoder architecture.
Utilized the student model as the teacher for network regularization in both encoder and decoder stages.
Focused on extracting enhanced feature representations and building a robust temporal decoder.

Main Results:

Validated the framework on the Cholec80 dataset, integrating it with four popular SOTA models.
Consistently improved performance across all integrated models.
Achieved significant boosts in accuracy and F1-score with the GRU model.

Conclusions:

Successfully integrated a self-knowledge distillation framework into surgical phase recognition training for the first time.
Demonstrated performance improvements in existing phase recognition models.
Showcased comparable performance to full-dataset training even with 75% of the training data.