Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Factors determining hydrocephalus after decompressive craniectomy: the role of interhemispheric hygroma.

Frontiers in neurology·2026
Same author

Study protocol for investigating real-world implementation of a combined glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) blood test in the management of adult mild traumatic brain injury in a single-centre European emergency department: the IMPACTS-BRAINI study.

BMJ open·2026
Same author

PituPhase65: An endoscopic pituitary surgery dataset for surgical phase recognition.

Scientific data·2026
Same author

High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging.

Bioengineering (Basel, Switzerland)·2026
Same author

Reappraisal of Computerized Tomography Scoring Systems for Outcome Prediction in Traumatic Brain Injury: A Comparative Analysis of Young and Older Adults.

Journal of neurotrauma·2026
Same author

Persistent sleep disturbances after mild traumatic brain injury: A prospective multimodal assessment with actigraphy and hormonal biomarkers.

Brain & spine·2026

Related Experiment Video

Updated: Jan 12, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Improving surgical phase recognition using self-supervised deep learning.

Alba Centeno López1,2, Ángela González-Cebrián3, Igor Paredes4,5

  • 1Computer Science and Engineering Department, Universidad Carlos III de Madrid, Madrid, Spain. alcenten@pa.uc3m.es.

Scientific Reports
|November 7, 2025
PubMed
Summary

Self-Supervised Learning (SSL) enhances Surgical Phase Recognition (SPR) in pituitary surgery, achieving higher accuracy than traditional methods. SSL also maintains performance with less labeled data, proving robust for surgical decision support systems.

Keywords:
Contrastive learningEndoscopic pituitary surgerySelf-supervised learningSurgical phase recognition

More Related Videos

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.0K

Related Experiment Videos

Last Updated: Jan 12, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.0K

Area of Science:

  • Medical Artificial Intelligence
  • Computer-Assisted Surgery
  • Surgical Workflow Analysis

Background:

  • Intelligent systems offer real-time decision support in surgery.
  • Surgical Phase Recognition (SPR) improves surgical workflows but is limited by data availability.
  • Self-Supervised Learning (SSL) leverages unlabeled data to learn representations, addressing data limitations.

Purpose of the Study:

  • To explore the application of SSL for SPR in endoscopic pituitary surgery.
  • To compare the performance of SimCLR and BYOL SSL frameworks for SPR.
  • To evaluate the impact of an attention-weighted pooling operator on SPR performance.

Main Methods:

  • Applied SimCLR and BYOL SSL frameworks to endoscopic pituitary surgery video data.
  • Integrated an attention-weighted pooling operator to enhance spatial feature extraction.
  • Evaluated performance on a downstream SPR task using F1-score, comparing with fully supervised learning and reduced data scenarios.

Main Results:

  • SimCLR with attention achieved a 66% F1-score, outperforming fully supervised learning (55%).
  • SSL maintained high performance (64% F1-score) even with a 50% reduction in labeled data.
  • SimCLR demonstrated greater robustness than BYOL across all evaluations.

Conclusions:

  • SSL is a robust approach for enhancing SPR in endoscopic pituitary surgery.
  • Integrating attention mechanisms further improves SSL performance for SPR.
  • SSL enables comparable SPR performance with significantly reduced labeled data requirements, facilitating advanced surgical decision support systems.