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Context-Aware Weakly Supervised Surgical Instrument Detection.

Renbo Li1, Zijian Zhao2, Feng Li3

  • 1School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, China.

Journal of Imaging Informatics in Medicine
|December 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces weakly supervised learning for surgical instrument detection, reducing the need for costly annotations. The new method accurately identifies instruments in minimally invasive surgery using only image-level data.

Keywords:
Minimally invasive surgeryRobot-assisted surgerySurgical tool detectionWeakly supervised

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Area of Science:

  • Medical image analysis
  • Computer vision in surgery
  • Minimally invasive surgery

Background:

  • Surgical instrument detection is crucial for operating room assistive systems.
  • Traditional methods require extensive, costly annotations.
  • Weakly supervised learning offers a solution by using image-level annotations.

Purpose of the Study:

  • To introduce weakly supervised object detection for surgical instrument identification.
  • To address the challenge of scarce annotated data in medical imaging.
  • To improve the accuracy and practicality of surgical instrument detection systems.

Main Methods:

  • Proposed a cross-image scenario-aware module to suppress background features.
  • Utilized contrastive loss instead of traditional multi-class label loss to enhance attention maps.
  • Developed a novel feature library construction and instance optimization architecture.

Main Results:

  • Achieved 60.4% mAP (mean Average Precision) on the m2cai16-tool-locations dataset.
  • Achieved 64.6% mCorLoc (mean Correct Localization) on the m2cai16-tool-locations dataset.
  • Demonstrated effective and practical performance for minimally invasive surgical instrument detection.

Conclusions:

  • Weakly supervised object detection is effective for surgical instrument identification.
  • The proposed methods significantly alleviate the problem of limited annotated data.
  • The approach shows promise for enhancing assistive systems in minimally invasive surgery.