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Updated: Jan 4, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Multi-instance multi-label learning for surgical image annotation.

Constantinos Loukas¹, Nicholas P Sgouros²

¹Laboratory of Medical Physics, Medical School National and Kapodistrian University of Athens, Athens, Greece.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|November 13, 2019

Summary

This summary is machine-generated.

This study introduces a new framework for multilabel annotation of laparoscopic images, improving the accuracy of identifying anatomical structures and tools in surgical videos.

Keywords:

anatomy annotation classification laparoscopy multi-instance multilabel learning surgical image analysis

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Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Limited research exists for multilabel annotation of still frames from laparoscopic videos.
Existing techniques primarily focus on phase and tool recognition from video sequences.

Purpose of the Study:

To develop a framework for multilabel annotation of still images from laparoscopic cholecystectomy (LC) videos.
To utilize multi-instance multiple-label learning for image annotation.
To compare different feature extraction and representation models.

Main Methods:

Images are treated as bags of features from segmented local regions.
Variational Bayesian Gaussian Mixture Models (VBGMM) are used for bag representation.
Three distinct techniques for feature extraction and bag representation were evaluated.

Main Results:

Successfully annotated four anatomical structures (abdominal wall, gallbladder, fat, liver bed) and a specimen bag in 482 images.
Achieved superior performance in single-label accuracy, with the highest score at 0.87.
Demonstrated over 20% improvement in four multilabel classification error metrics (one-error, ranking-loss, hamming-loss, coverage).

Conclusions:

The proposed approach offers an accurate and efficient method for image representation.
Enables effective multilabel classification of still images from laparoscopic surgery.
Advances the field of automated analysis for surgical videos.