Unsupervised Defect Detection for Surgical Instruments

• URL: http://arxiv.org/abs/2509.21561v1
• Date: Thu, 25 Sep 2025 20:40:52 GMT
• Title: Unsupervised Defect Detection for Surgical Instruments
• Authors: Joseph Huang, Yichi Zhang, Jingxi Yu, Wei Chen, Seunghyun Hwang, Qiang Qiu, Amy R. Reibman, Edward J. Delp, Fengqing Zhu,
• Abstract summary: We propose a versatile method that adapts unsupervised defect detection methods specifically for surgical instruments.<n>By integrating background masking, a patch-based analysis strategy, and efficient domain adaptation, our method overcomes these limitations.
• Score: 24.547211197229785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existing automated defect detection methods, typically trained on natural/industrial images, fail to transfer effectively to the surgical domain. We demonstrate that simply applying or fine-tuning these approaches leads to issues: false positive detections arising from textured backgrounds, poor sensitivity to small, subtle defects, and inadequate capture of instrument-specific features due to domain shift. To address these challenges, we propose a versatile method that adapts unsupervised defect detection methods specifically for surgical instruments. By integrating background masking, a patch-based analysis strategy, and efficient domain adaptation, our method overcomes these limitations, enabling the reliable detection of fine-grained defects in surgical instrument imagery.

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