Unsupervised Defect Detection for Surgical Instruments 事件

Unsupervised Defect Detection for Surgical Instruments arXiv:2509.21561v2 Announce Type: replace 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 detec