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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Tissue tracking under long-horizon occlusions with contrastive learning.

Myrto Inglezou1, Nikolaos Kegkeroglou2, Leonidas Delimpasis2

  • 1AI Innovation Centre, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK. mi23878@essex.ac.uk.

International Journal of Computer Assisted Radiology and Surgery
|March 6, 2026
PubMed
Summary

This study introduces a self-supervised method for tracking soft tissues in minimally invasive surgery, even after long-term occlusions. The system uses contrastive learning for robust re-identification, improving computer-assisted interventions.

Keywords:
Contrastive learningLong-horizon occlusionSelf-supervisedTissue tracking

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Area of Science:

  • Computer Vision
  • Medical Robotics
  • Surgical Navigation

Background:

  • Continuous soft-tissue tracking is vital for computer-assisted minimally invasive surgery.
  • Challenges include non-rigid deformation, camera motion, occlusions, and re-identification after long-horizon occlusions.

Purpose of the Study:

  • To develop a real-time tracking pipeline for soft tissues in minimally invasive surgery.
  • To address the challenge of long-horizon occlusions where tissues re-enter the field of view from different angles.
  • To enable robust re-identification of soft-tissue regions after extended periods of absence.

Main Methods:

  • A real-time pipeline integrating dense optical flow, monocular visual odometry, and a self-supervised template matching module.
  • Contrastive learning with a variational encoder trained using time cycle consistency for deformation-aware representations.
  • Self-supervised learning eliminates the need for manual annotations.

Main Results:

  • The proposed pipeline demonstrated stable tracking performance during extended occlusions and viewpoint changes.
  • Accurate re-identification of soft-tissue regions was achieved after reappearance.
  • The contrastive variational encoder improved robustness against tissue deformation and appearance variability compared to baseline methods.

Conclusions:

  • The framework offers a practical, self-supervised solution for long-horizon tissue tracking in minimally invasive surgery.
  • Promising performance was observed, although quantitative evaluation was limited to synthetic data.
  • The developed code is publicly available for further research and application.